A NOVEL APPROACH TO ACCESS CONTROL BASED ON FACE RECOGNITION

Size: px
Start display at page:

Download "A NOVEL APPROACH TO ACCESS CONTROL BASED ON FACE RECOGNITION"

Transcription

1 A NOVEL APPROACH TO ACCESS CONTROL BASED ON FACE RECOGNITION A. Hadid, M. Heikkilä, T. Ahonen, and M. Pietikäinen Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering P.O. Box 4500 FIN University of Oulu, Finland ABSTRACT In this paper, we introduce an approach to access control based on detecting and recognizing faces with novel methods. The goal is to build an automatic face recognition system which authorizes the access to the laboratory room for only certain persons while denying the access for the others. For this purpose, a camera is set in the door looking at the corridor and the captured frames are analyzed. The analysis starts by selecting the regions of interest in the images using either texture-based background subtraction or skin locus based color segmentation. Once the regions of interest are defined, a fast face detection scheme is launched. Then, the extracted faces are compared to those in the predefined database to determine whether the individuals are allowed to enter the room or not. Among the novelties of our approach is the use of the Local Binary Pattern (LBP) texture features for face recognition and background subtraction. 1. INTRODUCTION Proactive computing aims to design and develop smart environments which can adapt and adjust to the user s movements and actions without requiring any conscious control. In other words, the system should be able to identify the users, interpret their actions, and react appropriately. Thus, one of the first and most important building blocks of such environments is a person identification system. Face recognition has emerged as an adequate technology for person identification in such systems which do not require any user s cooperation. Indeed, face technology has several advantages over other biometric systems. First of all, a face recognition-based system is passive as there is no need to use our fingers or to say some words in order to be recognized by the system. Also, no expensive and specialized equipment are needed as a simple video camera connected to a personal computer are largely enough to build the system. Despite the significant progress made in face recognition research [1], the available commercial systems [2, 1] perform well only under relatively controlled environments. For instance, most of the applications of face recognition to access control suffer from either low recognition speed due to the complex analysis of facial images, or to the low recognition accuracy. The main methods employed by facial recognition vendors to identify and verify faces include eigenfaces [3], Elastic Bunch Graph Matching [4], and Local Feature Analysis (LFA) [5] which is claimed to be used in Visionics commercial system FaceIt. A list of some commercial systems is given in [1] and their performances are reported in the Face Recognition Vendor Test (FRVT) [2]. Recently, we have proposed a new approach for face recognition based on local binary patterns (LBP) [6]. Our method clearly outperformed the-state-of-the-art algorithms (PCA, EBGM and Bayesian Intra/extrapersonal classifier) on four standard FERET probe sets [7]. In this work, we describe an approach to access control based on detecting and recognizing faces with our newly proposed methods, allowing fast processing and accurate recognition. In the system, a camera is set in the door looking at the corridor and the captured frames are analyzed. Figure 1 shows the considered environment. The analysis starts by selecting the regions of interest in the image. For this purpose, two approaches were considered. The first one consists of extracting the moving objects from the video frames using a texture-based approach to background subtraction [8, 9]. The alternative is the use of skin segmentation, which is based on a robust modeling of skin color with so called skin locus used to extract the skin-like regions [10]. By adopting the latter approach, a set of skin-like regions, which are considered face candidates, are extracted from the video frames. After orientation normalization and based on verifying a set of criteria (face symmetry, presence of some facial features, variance of pixel intensities and connected component arrangement), only facial regions are selected [11]. To identify the faces, a new approach for face recognition is considered [6]. The face area is first divided into several blocks and then the LBP feature histograms [8] are extracted from each block and concatenated into a single global feature histogram which efficiently represents the face image. The recognition is performed by a simple histogram matching. The different phases are outlined in Sec-

2 Fig. 1. The environment: A camera is set in the door looking at the corridor and the captured images are processed in order to allow or deny the access to the laboratory room for the individuals Selecting the Regions of Interest Before launching the face detection, our approach starts by reducing the search space in order to speed-up processing and also to avoid some additional false alarms. We describe below the two approaches to reduce the search space and define the regions of interest Background Subtraction Fig. 2. Two schemes are considered: The first solution uses background subtraction in order to extract the regions of interest while the second is based on defining the regions of interest in the input image as those containing skin areas. The idea consists of searching for faces only among the moving objects. Therefore, we developed a novel blockbased approach for background subtraction in order to detect the moving objects [9]. tion FACE RECOGNITION-BASED ACCESS CONTROL Figure 2 outlines the different parts of our system. Two different schemes are shown: The first solution uses a novel texture-based approach to background subtraction in order to extract the regions of interest, which means the moving objects, from video frames. The alternative is based on defining the regions of interest in the input image as those containing skin areas. Fig. 3. An example of moving object extraction using texture-based background subtraction. In our approach, the background image is divided into equally sized blocks using partially overlapping grid structure and each block is modeled as a group of weighted adaptive LBP histograms [8]. The histograms are sorted in decreasing order according to the likelihood that they model the background and the first histograms are considered to be the background model. Foreground detection is achieved by comparing the histogram calculated for the new block

3 against the existing background histograms. If the match is found, the block is considered to belong to the background. Otherwise, the block is marked as foreground. The algorithm can adapt to inherent changes in the scene background (e.g. illumination changes and multi-modality) and manages situations where new stationary objects are introduced to or old removed from the background area. Moreover, it operates in real-time. Figure 3 shows an example of background subtraction result Skin Segmentation Instead of considering moving objects, the faces can be searched for among the skin-like regions. Therefore, our second approach consists of segmenting the input image by defining the skin areas. Although different people have different skin color, but several studies have shown that the major difference lies largely in their intensity rather than their chrominance. Several value distribution models have been compared in different color spaces (RGB, HSV, YCrCb, etc.). In our case, we use the skin locus which has performed well with images under widely varying conditions [10, 11]. Skin locus is the range of skin chromaticities under varying illumination/camera calibration conditions in NCC (normalized color coordinate) space. In the NCC space, the intensity is defined as I = R + G + B and chromaticities are r = R/I, g = G/I and b = B/I. Because r + g + b = 1, only the intensity and two chromaticity coordinates are enough for specifying any color uniquely. We considered r b coordinates to obtain both robustness against intensity variations and good overlap of chromaticities of different skin colors Face Detection After selecting the regions of interest in the input images, the next step is the detection of faces. We describe in the following our approach to face detection in case of skin segementation, which is the present implementation of the system. The skin segmentation phase results in a set of skinlike regions, which are considered face candidates. Using morphological operations (majority operator and applying dilations followed by erosions until the image no longer changes), we reduce the number of these regions. For every candidate, we verify whether it corresponds to a facial region or not. The verification scheme is summarized in Figure 4. To increase the speed and robustness of the detector, we organized some operations on a cascade structure. To deal with faces of different orientations, we firstly calculate the best ellipse fitting the face candidate. Based on the fact that the pixel value variations of other skin-like regions (such as hands) are smaller than those of face regions because of the Fig. 4. Our face detection scheme. presence of features with different brightness, we remove all face region candidates with pixel value variations smaller than a threshold. In order to improve the detection speed and achieve high robustness, we check the symmetry of the face and remove all the candidates when the symmetry is verified but no facial features are detected. Since it is not always possible to detect the facial features (due to different orientations, illuminations, etc.), we build a model of spatial arrangement of connected component features. The main steps of our face detector are described in Figure 4 and the details can be found in [11] Face Normalization and Recognition Once the face has been extracted, we compare the image to those stored in the database to determine the identity of the face. For this purpose, we first normalize of face image to have the same scale (144x112 pixels ) as that of the faces in the database. To recognize the face we adopted the new approach for face representation that we recently introduced [6]. The face area is first divided into several blocks and then the Local Binary Pattern features (LBP) are extracted from each block and concatenated into a single feature histogram which represents efficiently the face image. Figure. 5 describes the facial representation extraction. The recognition is performed by a simple histogram matching. We adopted the χ 2 (Chisquare) as dissimilarity metric for comparing a target face

4 3.3. Face Detection Results Fig. 5. Face image representation using LBP: The face area is first divided into 24 blocks and then the Local Binary Pattern features (LBP) are extracted from each block and concatenated into a single feature histogram. histogram S to a model histogram M: χ 2 (S, M) = l i=0 (S i M i ) 2 S i + M i, (1) where l is the length of feature vector used to represent the face image (in our experiments, l = 1416). 3. IMPLEMENTATION AND EXPERIMENTAL RESULTS 3.1. Face Database We applied our face detection approach (see Section 2.2) to extract the faces in both the training and testing videos % of the faces were successfully detected. Only few false positives were signaled. All these false detections were rejected during the recognition phase. See Figures 8 and 9 for some face detection examples. Note that the detection scheme is based on single frames and does not use the motion information Face Recognition Results First, we conducted a set of experiments in order to determine the parameters (LBP operator and window size) for the facial representation. By choosing a window size of 24*28 pixels and LBP8,1 u2 operator, each face image is represented by a feature vector of = 1416 elements. The adopted facial representation is shown in Figure 5. After selecting N b = 6 face models for each subject in the database, we considered an appearance-based face recognition scheme. We used the χ 2 (Chi-square) as a dissimilarity metric for comparing a target face histogram S to a model histogram M. Figure 7 shows the mean recognition rates (Rank curves) when testing the system with the second video sequence of each subject. Just for comparison, we also included the recognition results of PCA [3] and LDA [13] based approaches. To build the system, we collected 20 video sequences of 10 persons who are allowed to enter the laboratory room (2 videos per person). Each video sequence contains 351 frames. The resolution of the images is 640x480 pixels. The database includes frontal and near frontal views with different facial expressions. Some face images from one subject are shown in Figure 6. The database contains two videos per person. The first video is used for training and the other for testing Selecting Models for View-Based Face Recognition In our approach, we adopted a view-based face recognition. Therefore, we automatically selected a set of face models from each training video sequence of each subject. The extraction of the face models is performed using an unsupervised learning scheme that we recently proposed in [12]. The approach is based on applying the locally linear embedding algorithm (LLE) to the raw feature data and then performing K-means clustering in the obtained low dimensional space. We extracted Nb = 6 face models from each training sequence and used them in the appearance-based face recognition. Details about the model extraction process can be found in [12]. Fig. 7. Rank curves for the LBP, LDA and PCA methods. As shown in Figure 7, the LBP-based approach resulted in a recognition rate of 81.4% versus 64.2% and 76.8% for the PCA and LDA based methods, when trying to classify the test images into one of the 10 classes without considering the reject class (i.e. Rank(0)). By defining a threshold for the reject class, the recognition rates dropped to 66.9%, 58.1% and 49.0% for LBP, LDA and PCA methods, respectively. Using a K-nearest

5 Fig. 6. Examples of face images from the face database considered in our experiments. neighbor classifier (with K = 3), the recognition rates were 71.6% and 57.4% for LBP and PCA methods, respectively. Some examples of recognition using LBP-based approach are shown in Figures 8 and DISCUSSION AND CONCLUSION We presented a system using face recognition to access control. We introduced two schemes: the first one is based on using background subtraction to detect the moving objects followed by face detection and then by LBP-based face recognition. The second scheme consisted of performing skin segmentation instead of background subtraction. The former approach is more adequate for outdoor scenarios where the system might be faced to dramatic illumination changes and complex backgrounds. We reported the results using the latter scheme which is used for an indoor access control application. The system starts by finding the skin-like regions in the input images. This skin segmentation is based on a robust modeling of skin color with so called skin locus. Choosing skin the locus model is motivated by a previous extensive analysis, which showed its efficiency against the stateof-the-art [10]. Once the skin-like regions are segmented, a fast face detector is launched in order to verify whether the skin regions are faces or not [11]. After scale normalization, the faces are efficiently represented using LBP feature histograms [6, 8]. We collected 20 video sequences of 10 different persons (2 videos per person). From the first video sequence of each person, we automatically selected six face models to build a view-based face recognition. The model selection is based on an unsupervised learning scheme using Locally Linear Embedding for dimensionality reduction followed by K-means clustering [12]. We used the second video sequence of each person to test the system. The preliminary results showed a recognition rate of 71.6% using LBP-based face representation, K-nearest neighbors classifier and χ 2 as dissimilarity metric. Though the results are better than those obtained with PCA and LDA, but still they are preliminary and lot of improvements can be achieved. Indeed, some parameters of the system have been set by default and thus are not optimal. For instance, when dividing the facial images into several regions, we gave an equal weight to the contribution of each region. However, one may use different weights, depending on the role of the given regions in detection/recognition. For example, since the eye regions are important for recognition, a high weight can be attributed to the corresponding regions. Such a procedure enhanced the facial representation in [6]. Also, other metrics than χ 2 could be tested and adopted. It is worth to note that the different parts of the system have been tested earlier in extensive experiments. Our methods for background subtraction, skin detection and face recognition have outperformed the state-of-the-art methods, while the face detection scheme [11] has shown good results. In addition to the obtained promising results, an interesting characteristic of our system lies in its speed: the skin and face detections can be run in real time while the LBP feature histograms can be easily computed in a single scan through the images. All our analysis is based on processing single frames. Improvements could be obtained by exploiting information redundancy by choosing, for example, the good frames for recognition. An alternative consists of recognizing a set consecutive frames and then performing a voting strategy to find the identity of the face. The system recognizes some faces much easier than others and we reported the mean recognition rates. The lack of registration is likely to be the main reason of some recognition failures. A potential solution might be the extraction and tracking of facial features in order to align the faces before recognition. During the data acquisition, the subjects were asked to

6 Fig. 8. Two examples of successful recognition. read some text. This yielded in different facial expressions and also in a non-rigid motion of their facial features. It is of interest to study the incorporation of this dynamic information in recognition [14] and experiment with much larger databases. When using the background subtraction method for limiting the search areas, a different scheme from that described in this paper should be adopted for detecting the faces. For this purpose, our recently introduced approach to face detection using LBP features [15] is a suitable choice. The proposed detection method is based on encoding both local and global facial characteristics into a compact feature histogram using LBP and then scanning the search areas at different scales and positions to detect the faces. This approach has shown excellent results and outperformed the state-ofthe-art algorithms [15]. In some complex environments in which several parts of the background can be skin-like regions, one may combine background subtraction and skin color segmentation in order to select the regions of interest. First, background subtraction can be used to extract the moving objects and then skin detection can be used to find the skin-like regions among only the moving objects. In such a way, false detection alarms will be avoided. Acknowledgment This research was sponsored by the Academy of Finland and the Finnish Graduate School in Electronics, Telecommunications and Automation (GETA). 5. REFERENCES [1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, Face recognition: A literature survey, ACM Computing Surveys, vol. 34(4), pp , Dec [2] P.J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, and J. M. Bone, Face recognition vendor test 2002 results, Technical report, [3] M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, vol. 3, pp , [4] L. Wiskott, J.-M. Fellous, N. Kuiger, and C. von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp , [5] P. Penev and J.Atick, Local feature analysis: a general statistical theory for object representation, Network: Computation in Neural Systems, vol. 7, pp , [6] T. Ahonen, A. Hadid, and M. Pietikäinen, Face recognition with local binary patterns, in the 8th European Conference on Computer Vision, May 2004, vol. 1, pp [7] P.J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, The FERET evaluation methodology for facerecognition algorithms, IEEE Transactions on Pat-

7 Fig. 9. Examples where a stranger to the system is rejected (left) while an authorized person is recognized (right). tern Analysis and Machine Intelligence, vol. 22, pp , [8] T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp , [9] M. Heikkilä, M. Pietikäinen, and J. Heikkilä, A texture-based method for detecting moving objects, in British Machine Vision Conference (submitted), September [10] B. Martinkauppi, M. Soriano, and M. Pietikäinen, Detection of skin color under changing illumination: a comparative study, in 12th International Conference on Image Analysis and Processing (ICIAP 2003), September 2003, pp [11] A. Hadid, M. Pietikäinen, and B. Martinkauppi, Color-based face detection using skin locus model and hierarchical filtering, in Proc. 16th International Conference on Pattern Recognition, Quebec, 2002, vol. 4, pp [12] A. Hadid and M. Pietikäinen, Selecting models from videos for appearance-based face recognition, in Proc. the 17th International Conference on Pattern Recognition, August 2004, in press. [13] K. Etemad and R. Chellappa, Discriminant analysis for recognition of human face images, Journal of the Optical Society of America, vol. 14, pp , [14] A. Hadid and M. Pietikäinen, From still-image to video-based face recognition: An experimental analysis, in 6th Int. Conf. on Face and Gesture Recognition, 2004, pp [15] A. Hadid, M. Pietikäinen, and T. Ahonen, A discriminative feature space for detecting and recognizing faces, in Proc. Computer Vision and Pattern Recognition, June 2004, in press.

Selecting Models from Videos for Appearance-Based Face Recognition

Selecting Models from Videos for Appearance-Based Face Recognition Selecting Models from Videos for Appearance-Based Face Recognition Abdenour Hadid and Matti Pietikäinen Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering P.O.

More information

Texture Features in Facial Image Analysis

Texture Features in Facial Image Analysis Texture Features in Facial Image Analysis Matti Pietikäinen and Abdenour Hadid Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering P.O. Box 4500, FI-90014 University

More information

Better than best: matching score based face registration

Better than best: matching score based face registration Better than best: based face registration Luuk Spreeuwers University of Twente Fac. EEMCS, Signals and Systems Group Hogekamp Building, 7522 NB Enschede The Netherlands l.j.spreeuwers@ewi.utwente.nl Bas

More information

Learning to Recognize Faces in Realistic Conditions

Learning to Recognize Faces in Realistic Conditions 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

More information

ROBUST LDP BASED FACE DESCRIPTOR

ROBUST LDP BASED FACE DESCRIPTOR ROBUST LDP BASED FACE DESCRIPTOR Mahadeo D. Narlawar and Jaideep G. Rana Department of Electronics Engineering, Jawaharlal Nehru College of Engineering, Aurangabad-431004, Maharashtra, India ABSTRACT This

More information

Face Description with Local Binary Patterns: Application to Face Recognition

Face Description with Local Binary Patterns: Application to Face Recognition IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 28, NO. 12, DECEMBER 2006 2037 Face Description with Local Binary Patterns: Application to Face Recognition Timo Ahonen, Student Member,

More information

Decorrelated Local Binary Pattern for Robust Face Recognition

Decorrelated Local Binary Pattern for Robust Face Recognition International Journal of Advanced Biotechnology and Research (IJBR) ISSN 0976-2612, Online ISSN 2278 599X, Vol-7, Special Issue-Number5-July, 2016, pp1283-1291 http://www.bipublication.com Research Article

More information

Disguised Face Identification Based Gabor Feature and SVM Classifier

Disguised Face Identification Based Gabor Feature and SVM Classifier Disguised Face Identification Based Gabor Feature and SVM Classifier KYEKYUNG KIM, SANGSEUNG KANG, YUN KOO CHUNG and SOOYOUNG CHI Department of Intelligent Cognitive Technology Electronics and Telecommunications

More information

Face Recognition Using Phase-Based Correspondence Matching

Face Recognition Using Phase-Based Correspondence Matching Face Recognition Using Phase-Based Correspondence Matching Koichi Ito Takafumi Aoki Graduate School of Information Sciences, Tohoku University, 6-6-5, Aramaki Aza Aoba, Sendai-shi, 98 8579 Japan ito@aoki.ecei.tohoku.ac.jp

More information

Color Local Texture Features Based Face Recognition

Color Local Texture Features Based Face Recognition Color Local Texture Features Based Face Recognition Priyanka V. Bankar Department of Electronics and Communication Engineering SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India

More information

A comparative study of Four Neighbour Binary Patterns in Face Recognition

A comparative study of Four Neighbour Binary Patterns in Face Recognition A comparative study of Four Neighbour Binary Patterns in Face Recognition A. Geetha, 2 Y. Jacob Vetha Raj,2 Department of Computer Applications, Nesamony Memorial Christian College,Manonmaniam Sundaranar

More information

LOCAL APPEARANCE BASED FACE RECOGNITION USING DISCRETE COSINE TRANSFORM

LOCAL APPEARANCE BASED FACE RECOGNITION USING DISCRETE COSINE TRANSFORM LOCAL APPEARANCE BASED FACE RECOGNITION USING DISCRETE COSINE TRANSFORM Hazim Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs, University of Karlsruhe Am Fasanengarten 5, 76131, Karlsruhe, Germany

More information

Multidirectional 2DPCA Based Face Recognition System

Multidirectional 2DPCA Based Face Recognition System Multidirectional 2DPCA Based Face Recognition System Shilpi Soni 1, Raj Kumar Sahu 2 1 M.E. Scholar, Department of E&Tc Engg, CSIT, Durg 2 Associate Professor, Department of E&Tc Engg, CSIT, Durg Email:

More information

IMPROVED FACE RECOGNITION USING ICP TECHNIQUES INCAMERA SURVEILLANCE SYSTEMS. Kirthiga, M.E-Communication system, PREC, Thanjavur

IMPROVED FACE RECOGNITION USING ICP TECHNIQUES INCAMERA SURVEILLANCE SYSTEMS. Kirthiga, M.E-Communication system, PREC, Thanjavur IMPROVED FACE RECOGNITION USING ICP TECHNIQUES INCAMERA SURVEILLANCE SYSTEMS Kirthiga, M.E-Communication system, PREC, Thanjavur R.Kannan,Assistant professor,prec Abstract: Face Recognition is important

More information

A Texture-based Method for Detecting Moving Objects

A Texture-based Method for Detecting Moving Objects A Texture-based Method for Detecting Moving Objects Marko Heikkilä University of Oulu Machine Vision Group FINLAND Introduction The moving object detection, also called as background subtraction, is one

More information

Combining Gabor Features: Summing vs.voting in Human Face Recognition *

Combining Gabor Features: Summing vs.voting in Human Face Recognition * Combining Gabor Features: Summing vs.voting in Human Face Recognition * Xiaoyan Mu and Mohamad H. Hassoun Department of Electrical and Computer Engineering Wayne State University Detroit, MI 4822 muxiaoyan@wayne.edu

More information

Linear Discriminant Analysis in Ottoman Alphabet Character Recognition

Linear Discriminant Analysis in Ottoman Alphabet Character Recognition Linear Discriminant Analysis in Ottoman Alphabet Character Recognition ZEYNEB KURT, H. IREM TURKMEN, M. ELIF KARSLIGIL Department of Computer Engineering, Yildiz Technical University, 34349 Besiktas /

More information

Gabor Volume based Local Binary Pattern for Face Representation and Recognition

Gabor Volume based Local Binary Pattern for Face Representation and Recognition Gabor Volume based Local Binary Pattern for Face Representation and Recognition Zhen Lei 1 Shengcai Liao 1 Ran He 1 Matti Pietikäinen 2 Stan Z. Li 1 1 Center for Biometrics and Security Research & National

More information

Hierarchical Ensemble of Gabor Fisher Classifier for Face Recognition

Hierarchical Ensemble of Gabor Fisher Classifier for Face Recognition Hierarchical Ensemble of Gabor Fisher Classifier for Face Recognition Yu Su 1,2 Shiguang Shan,2 Xilin Chen 2 Wen Gao 1,2 1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin,

More information

An Adaptive Threshold LBP Algorithm for Face Recognition

An Adaptive Threshold LBP Algorithm for Face Recognition An Adaptive Threshold LBP Algorithm for Face Recognition Xiaoping Jiang 1, Chuyu Guo 1,*, Hua Zhang 1, and Chenghua Li 1 1 College of Electronics and Information Engineering, Hubei Key Laboratory of Intelligent

More information

Part-based Face Recognition Using Near Infrared Images

Part-based Face Recognition Using Near Infrared Images Part-based Face Recognition Using Near Infrared Images Ke Pan Shengcai Liao Zhijian Zhang Stan Z. Li Peiren Zhang University of Science and Technology of China Hefei 230026, China Center for Biometrics

More information

Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features

Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features 1 Kum Sharanamma, 2 Krishnapriya Sharma 1,2 SIR MVIT Abstract- To describe the image features the Local binary pattern (LBP)

More information

NIST. Support Vector Machines. Applied to Face Recognition U56 QC 100 NO A OS S. P. Jonathon Phillips. Gaithersburg, MD 20899

NIST. Support Vector Machines. Applied to Face Recognition U56 QC 100 NO A OS S. P. Jonathon Phillips. Gaithersburg, MD 20899 ^ A 1 1 1 OS 5 1. 4 0 S Support Vector Machines Applied to Face Recognition P. Jonathon Phillips U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Information

More information

Part-based Face Recognition Using Near Infrared Images

Part-based Face Recognition Using Near Infrared Images Part-based Face Recognition Using Near Infrared Images Ke Pan Shengcai Liao Zhijian Zhang Stan Z. Li Peiren Zhang University of Science and Technology of China Hefei 230026, China Center for Biometrics

More information

Weighted Multi-scale Local Binary Pattern Histograms for Face Recognition

Weighted Multi-scale Local Binary Pattern Histograms for Face Recognition Weighted Multi-scale Local Binary Pattern Histograms for Face Recognition Olegs Nikisins Institute of Electronics and Computer Science 14 Dzerbenes Str., Riga, LV1006, Latvia Email: Olegs.Nikisins@edi.lv

More information

A Hierarchical Face Identification System Based on Facial Components

A Hierarchical Face Identification System Based on Facial Components A Hierarchical Face Identification System Based on Facial Components Mehrtash T. Harandi, Majid Nili Ahmadabadi, and Babak N. Araabi Control and Intelligent Processing Center of Excellence Department of

More information

Illumination Invariant Face Recognition Based on Neural Network Ensemble

Illumination Invariant Face Recognition Based on Neural Network Ensemble Invariant Face Recognition Based on Network Ensemble Wu-Jun Li 1, Chong-Jun Wang 1, Dian-Xiang Xu 2, and Shi-Fu Chen 1 1 National Laboratory for Novel Software Technology Nanjing University, Nanjing 210093,

More information

Chapter 12. Face Analysis Using Local Binary Patterns

Chapter 12. Face Analysis Using Local Binary Patterns Chapter 12 Face Analysis Using Local Binary Patterns A. Hadid, G. Zhao, T. Ahonen, and M. Pietikäinen Machine Vision Group Infotech Oulu, P.O. Box 4500 FI-90014, University of Oulu, Finland http://www.ee.oulu.fi/mvg

More information

APPLICATION OF LOCAL BINARY PATTERN AND PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION

APPLICATION OF LOCAL BINARY PATTERN AND PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION APPLICATION OF LOCAL BINARY PATTERN AND PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION 1 CHETAN BALLUR, 2 SHYLAJA S S P.E.S.I.T, Bangalore Email: chetanballur7@gmail.com, shylaja.sharath@pes.edu Abstract

More information

Learning Human Identity using View-Invariant Multi-View Movement Representation

Learning Human Identity using View-Invariant Multi-View Movement Representation Learning Human Identity using View-Invariant Multi-View Movement Representation Alexandros Iosifidis, Anastasios Tefas, Nikolaos Nikolaidis and Ioannis Pitas Aristotle University of Thessaloniki Department

More information

A REAL-TIME FACIAL FEATURE BASED HEAD TRACKER

A REAL-TIME FACIAL FEATURE BASED HEAD TRACKER A REAL-TIME FACIAL FEATURE BASED HEAD TRACKER Jari Hannuksela, Janne Heikkilä and Matti Pietikäinen {jari.hannuksela, jth, mkp}@ee.oulu.fi Machine Vision Group, Infotech Oulu P.O. Box 45, FIN-914 University

More information

Component-based Face Recognition with 3D Morphable Models

Component-based Face Recognition with 3D Morphable Models Component-based Face Recognition with 3D Morphable Models B. Weyrauch J. Huang benjamin.weyrauch@vitronic.com jenniferhuang@alum.mit.edu Center for Biological and Center for Biological and Computational

More information

Countermeasure for the Protection of Face Recognition Systems Against Mask Attacks

Countermeasure for the Protection of Face Recognition Systems Against Mask Attacks Countermeasure for the Protection of Face Recognition Systems Against Mask Attacks Neslihan Kose, Jean-Luc Dugelay Multimedia Department EURECOM Sophia-Antipolis, France {neslihan.kose, jean-luc.dugelay}@eurecom.fr

More information

Texture Classification by Combining Local Binary Pattern Features and a Self-Organizing Map

Texture Classification by Combining Local Binary Pattern Features and a Self-Organizing Map Texture Classification by Combining Local Binary Pattern Features and a Self-Organizing Map Markus Turtinen, Topi Mäenpää, and Matti Pietikäinen Machine Vision Group, P.O.Box 4500, FIN-90014 University

More information

Three-Dimensional Face Recognition: A Fishersurface Approach

Three-Dimensional Face Recognition: A Fishersurface Approach Three-Dimensional Face Recognition: A Fishersurface Approach Thomas Heseltine, Nick Pears, Jim Austin Department of Computer Science, The University of York, United Kingdom Abstract. Previous work has

More information

AN EXAMINING FACE RECOGNITION BY LOCAL DIRECTIONAL NUMBER PATTERN (Image Processing)

AN EXAMINING FACE RECOGNITION BY LOCAL DIRECTIONAL NUMBER PATTERN (Image Processing) AN EXAMINING FACE RECOGNITION BY LOCAL DIRECTIONAL NUMBER PATTERN (Image Processing) J.Nithya 1, P.Sathyasutha2 1,2 Assistant Professor,Gnanamani College of Engineering, Namakkal, Tamil Nadu, India ABSTRACT

More information

Face and Nose Detection in Digital Images using Local Binary Patterns

Face and Nose Detection in Digital Images using Local Binary Patterns Face and Nose Detection in Digital Images using Local Binary Patterns Stanko Kružić Post-graduate student University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture

More information

On Modeling Variations for Face Authentication

On Modeling Variations for Face Authentication On Modeling Variations for Face Authentication Xiaoming Liu Tsuhan Chen B.V.K. Vijaya Kumar Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 xiaoming@andrew.cmu.edu

More information

A New Feature Local Binary Patterns (FLBP) Method

A New Feature Local Binary Patterns (FLBP) Method A New Feature Local Binary Patterns (FLBP) Method Jiayu Gu and Chengjun Liu The Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA Abstract - This paper presents

More information

Dr. Enrique Cabello Pardos July

Dr. Enrique Cabello Pardos July Dr. Enrique Cabello Pardos July 20 2011 Dr. Enrique Cabello Pardos July 20 2011 ONCE UPON A TIME, AT THE LABORATORY Research Center Contract Make it possible. (as fast as possible) Use the best equipment.

More information

Face Recognition with Local Line Binary Pattern

Face Recognition with Local Line Binary Pattern 2009 Fifth International Conference on Image and Graphics Face Recognition with Local Line Binary Pattern Amnart Petpon and Sanun Srisuk Department of Computer Engineering, Mahanakorn University of Technology

More information

Face Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier Rong-sheng LI, Fei-fei LEE *, Yan YAN and Qiu CHEN

Face Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier Rong-sheng LI, Fei-fei LEE *, Yan YAN and Qiu CHEN 2016 International Conference on Artificial Intelligence: Techniques and Applications (AITA 2016) ISBN: 978-1-60595-389-2 Face Recognition Using Vector Quantization Histogram and Support Vector Machine

More information

FACE RECOGNITION USING SUPPORT VECTOR MACHINES

FACE RECOGNITION USING SUPPORT VECTOR MACHINES FACE RECOGNITION USING SUPPORT VECTOR MACHINES Ashwin Swaminathan ashwins@umd.edu ENEE633: Statistical and Neural Pattern Recognition Instructor : Prof. Rama Chellappa Project 2, Part (b) 1. INTRODUCTION

More information

Object detection using non-redundant local Binary Patterns

Object detection using non-redundant local Binary Patterns University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2010 Object detection using non-redundant local Binary Patterns Duc Thanh

More information

Palm Vein Recognition with Local Binary Patterns and Local Derivative Patterns

Palm Vein Recognition with Local Binary Patterns and Local Derivative Patterns Palm Vein Recognition with Local Binary Patterns and Local Derivative Patterns Leila Mirmohamadsadeghi and Andrzej Drygajlo Swiss Federal Institude of Technology Lausanne (EPFL) CH-1015 Lausanne, Switzerland

More information

Face Recognition Based On Granular Computing Approach and Hybrid Spatial Features

Face Recognition Based On Granular Computing Approach and Hybrid Spatial Features Face Recognition Based On Granular Computing Approach and Hybrid Spatial Features S.Sankara vadivu 1, K. Aravind Kumar 2 Final Year Student of M.E, Department of Computer Science and Engineering, Manonmaniam

More information

A Real Time Facial Expression Classification System Using Local Binary Patterns

A Real Time Facial Expression Classification System Using Local Binary Patterns A Real Time Facial Expression Classification System Using Local Binary Patterns S L Happy, Anjith George, and Aurobinda Routray Department of Electrical Engineering, IIT Kharagpur, India Abstract Facial

More information

Dealing with Inaccurate Face Detection for Automatic Gender Recognition with Partially Occluded Faces

Dealing with Inaccurate Face Detection for Automatic Gender Recognition with Partially Occluded Faces Dealing with Inaccurate Face Detection for Automatic Gender Recognition with Partially Occluded Faces Yasmina Andreu, Pedro García-Sevilla, and Ramón A. Mollineda Dpto. Lenguajes y Sistemas Informáticos

More information

Dynamic skin detection in color images for sign language recognition

Dynamic skin detection in color images for sign language recognition Dynamic skin detection in color images for sign language recognition Michal Kawulok Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland michal.kawulok@polsl.pl

More information

Graph Matching Iris Image Blocks with Local Binary Pattern

Graph Matching Iris Image Blocks with Local Binary Pattern Graph Matching Iris Image Blocs with Local Binary Pattern Zhenan Sun, Tieniu Tan, and Xianchao Qiu Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of

More information

Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction

Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction Ham Rara, Shireen Elhabian, Asem Ali University of Louisville Louisville, KY {hmrara01,syelha01,amali003}@louisville.edu Mike Miller,

More information

SELECTION OF THE OPTIMAL PARAMETER VALUE FOR THE LOCALLY LINEAR EMBEDDING ALGORITHM. Olga Kouropteva, Oleg Okun and Matti Pietikäinen

SELECTION OF THE OPTIMAL PARAMETER VALUE FOR THE LOCALLY LINEAR EMBEDDING ALGORITHM. Olga Kouropteva, Oleg Okun and Matti Pietikäinen SELECTION OF THE OPTIMAL PARAMETER VALUE FOR THE LOCALLY LINEAR EMBEDDING ALGORITHM Olga Kouropteva, Oleg Okun and Matti Pietikäinen Machine Vision Group, Infotech Oulu and Department of Electrical and

More information

Human Motion Detection and Tracking for Video Surveillance

Human Motion Detection and Tracking for Video Surveillance Human Motion Detection and Tracking for Video Surveillance Prithviraj Banerjee and Somnath Sengupta Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur,

More information

REAL-TIME FACE AND HAND DETECTION FOR VIDEOCONFERENCING ON A MOBILE DEVICE. Frank M. Ciaramello and Sheila S. Hemami

REAL-TIME FACE AND HAND DETECTION FOR VIDEOCONFERENCING ON A MOBILE DEVICE. Frank M. Ciaramello and Sheila S. Hemami REAL-TIME FACE AND HAND DETECTION FOR VIDEOCONFERENCING ON A MOBILE DEVICE Frank M. Ciaramello and Sheila S. Hemami Visual Communication Laboratory School of Electrical and Computer Engineering, Cornell

More information

Image Based Feature Extraction Technique For Multiple Face Detection and Recognition in Color Images

Image Based Feature Extraction Technique For Multiple Face Detection and Recognition in Color Images Image Based Feature Extraction Technique For Multiple Face Detection and Recognition in Color Images 1 Anusha Nandigam, 2 A.N. Lakshmipathi 1 Dept. of CSE, Sir C R Reddy College of Engineering, Eluru,

More information

Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels

Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels Stan Z. Li, ChunShui Zhao, Meng Ao, Zhen Lei Center for Biometrics and Security Research & National Laboratory of Pattern

More information

An Automatic Face Recognition System in the Near Infrared Spectrum

An Automatic Face Recognition System in the Near Infrared Spectrum An Automatic Face Recognition System in the Near Infrared Spectrum Shuyan Zhao and Rolf-Rainer Grigat Technical University Hamburg Harburg Vision Systems, 4-08/1 Harburger Schloßstr 20 21079 Hamburg, Germany

More information

FACE RECOGNITION BASED ON LOCAL DERIVATIVE TETRA PATTERN

FACE RECOGNITION BASED ON LOCAL DERIVATIVE TETRA PATTERN ISSN: 976-92 (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, FEBRUARY 27, VOLUME: 7, ISSUE: 3 FACE RECOGNITION BASED ON LOCAL DERIVATIVE TETRA PATTERN A. Geetha, M. Mohamed Sathik 2 and Y. Jacob

More information

Experimentation on the use of Chromaticity Features, Local Binary Pattern and Discrete Cosine Transform in Colour Texture Analysis

Experimentation on the use of Chromaticity Features, Local Binary Pattern and Discrete Cosine Transform in Colour Texture Analysis Experimentation on the use of Chromaticity Features, Local Binary Pattern and Discrete Cosine Transform in Colour Texture Analysis N.Padmapriya, Ovidiu Ghita, and Paul.F.Whelan Vision Systems Laboratory,

More information

Detecting motion by means of 2D and 3D information

Detecting motion by means of 2D and 3D information Detecting motion by means of 2D and 3D information Federico Tombari Stefano Mattoccia Luigi Di Stefano Fabio Tonelli Department of Electronics Computer Science and Systems (DEIS) Viale Risorgimento 2,

More information

Combined Histogram-based Features of DCT Coefficients in Low-frequency Domains for Face Recognition

Combined Histogram-based Features of DCT Coefficients in Low-frequency Domains for Face Recognition Combined Histogram-based Features of DCT Coefficients in Low-frequency Domains for Face Recognition Qiu Chen, Koji Kotani *, Feifei Lee, and Tadahiro Ohmi New Industry Creation Hatchery Center, Tohoku

More information

Mouse Pointer Tracking with Eyes

Mouse Pointer Tracking with Eyes Mouse Pointer Tracking with Eyes H. Mhamdi, N. Hamrouni, A. Temimi, and M. Bouhlel Abstract In this article, we expose our research work in Human-machine Interaction. The research consists in manipulating

More information

Robust Face Detection Based on Convolutional Neural Networks

Robust Face Detection Based on Convolutional Neural Networks Robust Face Detection Based on Convolutional Neural Networks M. Delakis and C. Garcia Department of Computer Science, University of Crete P.O. Box 2208, 71409 Heraklion, Greece {delakis, cgarcia}@csd.uoc.gr

More information

Recognition of Non-symmetric Faces Using Principal Component Analysis

Recognition of Non-symmetric Faces Using Principal Component Analysis Recognition of Non-symmetric Faces Using Principal Component Analysis N. Krishnan Centre for Information Technology & Engineering Manonmaniam Sundaranar University, Tirunelveli-627012, India Krishnan17563@yahoo.com

More information

Linear Discriminant Analysis for 3D Face Recognition System

Linear Discriminant Analysis for 3D Face Recognition System Linear Discriminant Analysis for 3D Face Recognition System 3.1 Introduction Face recognition and verification have been at the top of the research agenda of the computer vision community in recent times.

More information

Semi-Supervised PCA-based Face Recognition Using Self-Training

Semi-Supervised PCA-based Face Recognition Using Self-Training Semi-Supervised PCA-based Face Recognition Using Self-Training Fabio Roli and Gian Luca Marcialis Dept. of Electrical and Electronic Engineering, University of Cagliari Piazza d Armi, 09123 Cagliari, Italy

More information

Head Frontal-View Identification Using Extended LLE

Head Frontal-View Identification Using Extended LLE Head Frontal-View Identification Using Extended LLE Chao Wang Center for Spoken Language Understanding, Oregon Health and Science University Abstract Automatic head frontal-view identification is challenging

More information

Implementation of a Face Recognition System for Interactive TV Control System

Implementation of a Face Recognition System for Interactive TV Control System Implementation of a Face Recognition System for Interactive TV Control System Sang-Heon Lee 1, Myoung-Kyu Sohn 1, Dong-Ju Kim 1, Byungmin Kim 1, Hyunduk Kim 1, and Chul-Ho Won 2 1 Dept. IT convergence,

More information

Face Detection and Recognition in an Image Sequence using Eigenedginess

Face Detection and Recognition in an Image Sequence using Eigenedginess Face Detection and Recognition in an Image Sequence using Eigenedginess B S Venkatesh, S Palanivel and B Yegnanarayana Department of Computer Science and Engineering. Indian Institute of Technology, Madras

More information

A New Gabor Phase Difference Pattern for Face and Ear Recognition

A New Gabor Phase Difference Pattern for Face and Ear Recognition A New Gabor Phase Difference Pattern for Face and Ear Recognition Yimo Guo 1,, Guoying Zhao 1, Jie Chen 1, Matti Pietikäinen 1 and Zhengguang Xu 1 Machine Vision Group, Department of Electrical and Information

More information

Manifold Learning for Video-to-Video Face Recognition

Manifold Learning for Video-to-Video Face Recognition Manifold Learning for Video-to-Video Face Recognition Abstract. We look in this work at the problem of video-based face recognition in which both training and test sets are video sequences, and propose

More information

Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor

Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor Author Zhang, Baochang, Gao, Yongsheng, Zhao, Sanqiang, Liu, Jianzhuang Published 2010 Journal

More information

II. LITERATURE REVIEW

II. LITERATURE REVIEW Matlab Implementation of Face Recognition Using Local Binary Variance Pattern N.S.Gawai 1, V.R.Pandit 2, A.A.Pachghare 3, R.G.Mundada 4, S.A.Fanan 5 1,2,3,4,5 Department of Electronics & Telecommunication

More information

International Journal of Modern Trends in Engineering and Research e-issn No.: , Date: 2-4 July, 2015

International Journal of Modern Trends in Engineering and Research   e-issn No.: , Date: 2-4 July, 2015 International Journal of Modern Trends in Engineering and Research www.ijmter.com e-issn No.:2349-9745, Date: 2-4 July, 2015 FACE RECOGNITION IN ANDROID K.M. Sanghavi 1, Agrawal Mohini 2,Bafna Khushbu

More information

Face Identification by a Cascade of Rejection Classifiers

Face Identification by a Cascade of Rejection Classifiers Boston U. Computer Science Tech. Report No. BUCS-TR-2005-022, Jun. 2005. To appear in Proc. IEEE Workshop on Face Recognition Grand Challenge Experiments, Jun. 2005. Face Identification by a Cascade of

More information

Face Recognition with Local Binary Patterns

Face Recognition with Local Binary Patterns Face Recognition with Local Binary Patterns Bachelor Assignment B.K. Julsing University of Twente Department of Electrical Engineering, Mathematics & Computer Science (EEMCS) Signals & Systems Group (SAS)

More information

3D Face Modelling Under Unconstrained Pose & Illumination

3D Face Modelling Under Unconstrained Pose & Illumination David Bryan Ottawa-Carleton Institute for Biomedical Engineering Department of Systems and Computer Engineering Carleton University January 12, 2009 Agenda Problem Overview 3D Morphable Model Fitting Model

More information

Applications Video Surveillance (On-line or off-line)

Applications Video Surveillance (On-line or off-line) Face Face Recognition: Dimensionality Reduction Biometrics CSE 190-a Lecture 12 CSE190a Fall 06 CSE190a Fall 06 Face Recognition Face is the most common biometric used by humans Applications range from

More information

An Integrated Face Recognition Algorithm Based on Wavelet Subspace

An Integrated Face Recognition Algorithm Based on Wavelet Subspace , pp.20-25 http://dx.doi.org/0.4257/astl.204.48.20 An Integrated Face Recognition Algorithm Based on Wavelet Subspace Wenhui Li, Ning Ma, Zhiyan Wang College of computer science and technology, Jilin University,

More information

MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION

MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION Panca Mudjirahardjo, Rahmadwati, Nanang Sulistiyanto and R. Arief Setyawan Department of Electrical Engineering, Faculty of

More information

A Texture-Based Method for Modeling the Background and Detecting Moving Objects

A Texture-Based Method for Modeling the Background and Detecting Moving Objects A Texture-Based Method for Modeling the Background and Detecting Moving Objects Marko Heikkilä and Matti Pietikäinen, Senior Member, IEEE 2 Abstract This paper presents a novel and efficient texture-based

More information

Artifacts and Textured Region Detection

Artifacts and Textured Region Detection Artifacts and Textured Region Detection 1 Vishal Bangard ECE 738 - Spring 2003 I. INTRODUCTION A lot of transformations, when applied to images, lead to the development of various artifacts in them. In

More information

Haresh D. Chande #, Zankhana H. Shah *

Haresh D. Chande #, Zankhana H. Shah * Illumination Invariant Face Recognition System Haresh D. Chande #, Zankhana H. Shah * # Computer Engineering Department, Birla Vishvakarma Mahavidyalaya, Gujarat Technological University, India * Information

More information

Principal Component Analysis and Neural Network Based Face Recognition

Principal Component Analysis and Neural Network Based Face Recognition Principal Component Analysis and Neural Network Based Face Recognition Qing Jiang Mailbox Abstract People in computer vision and pattern recognition have been working on automatic recognition of human

More information

Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation

Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation Xiujuan Chai 1, 2, Shiguang Shan 2, Wen Gao 1, 2 1 Vilab, Computer College, Harbin Institute of Technology, Harbin,

More information

A Texture-based Method for Detecting Moving Objects

A Texture-based Method for Detecting Moving Objects A Texture-based Method for Detecting Moving Objects M. Heikkilä, M. Pietikäinen and J. Heikkilä Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering P.O. Box 4500

More information

Face Recognition Using Long Haar-like Filters

Face Recognition Using Long Haar-like Filters Face Recognition Using Long Haar-like Filters Y. Higashijima 1, S. Takano 1, and K. Niijima 1 1 Department of Informatics, Kyushu University, Japan. Email: {y-higasi, takano, niijima}@i.kyushu-u.ac.jp

More information

A face recognition system based on local feature analysis

A face recognition system based on local feature analysis A face recognition system based on local feature analysis Stefano Arca, Paola Campadelli, Raffaella Lanzarotti Dipartimento di Scienze dell Informazione Università degli Studi di Milano Via Comelico, 39/41

More information

Robust color segmentation algorithms in illumination variation conditions

Robust color segmentation algorithms in illumination variation conditions 286 CHINESE OPTICS LETTERS / Vol. 8, No. / March 10, 2010 Robust color segmentation algorithms in illumination variation conditions Jinhui Lan ( ) and Kai Shen ( Department of Measurement and Control Technologies,

More information

Image Processing and Image Representations for Face Recognition

Image Processing and Image Representations for Face Recognition Image Processing and Image Representations for Face Recognition 1 Introduction Face recognition is an active area of research in image processing and pattern recognition. Since the general topic of face

More information

Hybrid Face Recognition and Classification System for Real Time Environment

Hybrid Face Recognition and Classification System for Real Time Environment Hybrid Face Recognition and Classification System for Real Time Environment Dr.Matheel E. Abdulmunem Department of Computer Science University of Technology, Baghdad, Iraq. Fatima B. Ibrahim Department

More information

Human Face Recognition Using Weighted Vote of Gabor Magnitude Filters

Human Face Recognition Using Weighted Vote of Gabor Magnitude Filters Human Face Recognition Using Weighted Vote of Gabor Magnitude Filters Iqbal Nouyed, Graduate Student member IEEE, M. Ashraful Amin, Member IEEE, Bruce Poon, Senior Member IEEE, Hong Yan, Fellow IEEE Abstract

More information

Detecting and Identifying Moving Objects in Real-Time

Detecting and Identifying Moving Objects in Real-Time Chapter 9 Detecting and Identifying Moving Objects in Real-Time For surveillance applications or for human-computer interaction, the automated real-time tracking of moving objects in images from a stationary

More information

Morphological Change Detection Algorithms for Surveillance Applications

Morphological Change Detection Algorithms for Surveillance Applications Morphological Change Detection Algorithms for Surveillance Applications Elena Stringa Joint Research Centre Institute for Systems, Informatics and Safety TP 270, Ispra (VA), Italy elena.stringa@jrc.it

More information

Face Recognition for Mobile Devices

Face Recognition for Mobile Devices Face Recognition for Mobile Devices Aditya Pabbaraju (adisrinu@umich.edu), Srujankumar Puchakayala (psrujan@umich.edu) INTRODUCTION Face recognition is an application used for identifying a person from

More information

MULTI-VIEW FACE DETECTION AND POSE ESTIMATION EMPLOYING EDGE-BASED FEATURE VECTORS

MULTI-VIEW FACE DETECTION AND POSE ESTIMATION EMPLOYING EDGE-BASED FEATURE VECTORS MULTI-VIEW FACE DETECTION AND POSE ESTIMATION EMPLOYING EDGE-BASED FEATURE VECTORS Daisuke Moriya, Yasufumi Suzuki, and Tadashi Shibata Masakazu Yagi and Kenji Takada Department of Frontier Informatics,

More information

Short Survey on Static Hand Gesture Recognition

Short Survey on Static Hand Gesture Recognition Short Survey on Static Hand Gesture Recognition Huu-Hung Huynh University of Science and Technology The University of Danang, Vietnam Duc-Hoang Vo University of Science and Technology The University of

More information

A Hybrid Face Detection System using combination of Appearance-based and Feature-based methods

A Hybrid Face Detection System using combination of Appearance-based and Feature-based methods IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.5, May 2009 181 A Hybrid Face Detection System using combination of Appearance-based and Feature-based methods Zahra Sadri

More information

Mobile Face Recognization

Mobile Face Recognization Mobile Face Recognization CS4670 Final Project Cooper Bills and Jason Yosinski {csb88,jy495}@cornell.edu December 12, 2010 Abstract We created a mobile based system for detecting faces within a picture

More information

Organic Computing for face and object recognition

Organic Computing for face and object recognition Organic Computing for face and object recognition Rolf P. Würtz Institut für Neuroinformatik, Ruhr-Universität Bochum Abstract: In this paper I describe how the various subsystems for a vision system capable

More information