Computer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 8. Face recognition attendance system based on PCA approach
|
|
- Jeffrey Waters
- 5 years ago
- Views:
Transcription
1 Computer Aided Drafting, Design and Manufacturing Volume 6, Number, June 016, Page 8 CADDM Face recognition attendance system based on PCA approach Li Yanling 1,, Chen Yisong, Wang Guoping 1. Department of Computer, Changzhi College, Changzhi , China;. School of Electronics Engineering and Computer Science, Peking University, Beijing , China. Abstract: This paper uses principal component analysis (PCA) to train the face and extract the characteristic value. This approach achieves the purpose of rapid attendance. PCA is an early and important approach for face recognization. It can reduce the dimension of face image space as well as describe the variation characteristics between different face images. The attendance system is a realtime system that requires shorter response time, for which PCA is a best choice. We use histogram equalization to eliminate the noise and improve the performance. With convenient MATLAB GUI visual operation interface, users can click on the corresponding button to implement face recognition tasks. Key words: face recognition; principal component analysis; eigenface 1 Introduction With the explosion of information technology, efficient and convenient identification has been a critical social problem to be solved. Using ID and password for authentication has gradually brought more and more problems. There are many problems of the existing system that don t meet the requirement of the rapid development of the society. Such as inconvenience, memory problems, and the risk of loss, theft, etc. The traditional methods adopted biometric authentication technology. Among them, the face recognition technology has become the representative of the identity authentication technology due to its unique advantages. Modern attendance system represented by the face attendance system, this kind of system using face recognition for attendance is widely used. It is convenient, and the staff do not need to carry the card. Firstly, Kirby and Sirovich [1] use the principal component analysis (PCA) technology to solve the optimal problems of face image. Based on their theory, Turk and Pentland [] use it in the field of face recognition in 1991, called eigenface method. In recent years, the two-dimensional principal component analysis method (DPCA) gradually caused widespread concern. Yang first proposes this method, and his main job is to construct covariance matrix of the image directly from the original -D image. Viola and Jones [3] described a visual object detection framework that is capable of processing images extremely rapid while achieving high detection rates. The face recognition method using PCA with neural network back error propagation learning algorithm is proposed by Ruprah [4], in his paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. Nawaz et al. [5] proposed a face recognition system based on PCA. The system consists of a database of a set of facial patterns for each individual. The characteristic features called eigenfaces are extracted from the stored images, which is trained for subsequent recognition. Gross et al. [6] investigate recognition system of faces for meeting. They propose a novel algorithim for environment challenges in which combine local features under certain constraints. The existing system can not handle new face images that not stored in the database, and its performance is not appreciable. This paper is to establish a set of system structure which is stable, reliable and practical. It provides good support for the use of units of the personnel management. The Project item: Supported by Higher School Science and Technology Innovation Fund Project (013160) and Changzhi College Teaching Reform Fund Project(JY01503). Corresponding author: Li Yanling, Female, Master, Associate Professor, hhly11109@163.com.
2 Li Yanling et al., Face recognition attendance system based on PCA approach 9 proposed system overcomes some limitations, for example it extracts main features rather than the whole image by which discriminatory power is improved. The structure of system function is shown in Fig.1. This system mainly includes three functions: registration module, attendance module and management module. Among them, face recognition in attendance module consists of the face data acquition, storage and the comparision function. Fig. 1 Block diagram of function structure. Face recognition by PCA.1 Face algorithm introduction Face recognition is a complex process. The computer face recognition consists of several steps. At first, we do the face detection among the collecting images to make sure if there is any face in the images. Secondly, we extract the face after finding the position of face. Usually extracted face can be recognized by combining detection and location. That is to say, we can confirm the identity of the face by extracting feature [7]. At the registration step, face information is acquired. Usually every staff need to collect 15 faces, and exists in the database with employee information. Face information management can replaced with new faces information collected by cover operations. Principal component analysis through the K-L mapped matrix of target image data to a small number matrix space, so as to realize the multidimensional number matrix data dimension reduction processing. This algorithm can obtain the largest dimensionality reduction under the condition of least information loss, and get the feature vectors we need [8]. It is a mathematical procedure that extracts the principle features in the multi-dimensional data. First the face image is projected, then the eigenface space and the position of the face in the database are compared [9]. Suppose there are n training sample, of which every sample compose the pixel x i. The number of sample image pixel is the dimension of vector x i. The vector sample set is{x 1,X,X 3,,X N }, its average vector of the sample set as follows: 1 n i n i 1 X x (1) The deviation between training sample and average face is y i, deviation matrix of the sample set is C m*n, covariance matrix of the sample is formed according to following formula: 1 n T yy i i i 1 n () We calculate the eigenvector and the corresponding eigenvalues of the covariance matrix. Image information are mainly concentrated in the feature vector, by comparing the feature vectors to find matching images.. Face register When a user registers in the system, the camera is called to capture 15 images of his face. At the same time, the user should fill in corresponding information to complete the registration. In order to guarantee the reliability of the face
3 10 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.6, No., Jun. 016 recognition, reduce the secondary factors influence on identification, some rule must be obeyed, the background of the face image must be white, and the distance between face and camera is about 40 centimeters, face fill the full screen in the up and down direction. After clicking on the button of registration, the user clicks on the button of image acquisition, as shown in Fig.. () If < and i i, the input image contains unknown face; (3) If < and i i<, the input image is the face of the k-th person in the data set. Image acquisition module will automatically open the camera and will take pictures of the real time interface, it is shown in the image preview interface, as shown in Fig.3. Fig. Interface of registration..3 Attendance operation After registration, we can undertake attendance sign-in operation, the steps are as follows [10] : (1) The difference of image face and average face project into the feature space, get its eigenvector: T w (3) () Defining the threshold: 1 max i j ( i, j 1,,...,00) (4) i, j (3) Using Euclidean distance to calculate the distance of and each face i : i i ( i 1,,...,00) (5) In order to distinguish face and nonface, still need to calculate the distance between original image f and reconstruction image of eigenface space: Among them: f (6) (7) f w The image is classified according to the following rules: (1) If, the input image doesn t include any face object; Fig. 3 Interface of image acquisition. Thus obtain the matching image data, to show the user as a result, the completion of image recognition, click access information to complete face recognition, information bar displays user information, as shown in Fig.4. Fig. 4 Information of acquisition. The user can undertake attendance sign in or sign out, the system automatically records sign in and sign out time, to determine whether he s late or not. When a user sign-in time for non-work time automatically, it s recorded for being late, as shown in Fig.5.
4 Li Yanling et al., Face recognition attendance system based on PCA approach 11 desired effect. But the histogram normalization can be used and get the needed shape. 3. Equalization and normalization First RGB image is converted to gray image, which is shown in Fig.6. Fig. 5 Successfully interface of signature. Fig. 6 The conversion of RGB to gray. 3 Noise processing 3.1 Histogram introduction In some cases, the collected images are usually influenced by something, such as illumination, poses, expressions, glasses, moustache, hats, etc. Generally, the brightness of image affect the effect of the final recognition. Use histogram equalization, increase the gray dynamic range image, image contrast. The basic idea is let y pixel values in new distribution evenly as possible [11-1]. In short, the histogram recorded the number of pixels, statistical values stored in array structure, through image statistical characteristics transformation in the form of a reference image [13]. Histogram is defined as: nk Pr ( k ) (8) N Among them, n k express the number of gray pixel level for r k, N as the total number of pixels in the image. Histogram with horizontal grayscale, gray frequency expressed in the vertical. Histogram equalization process using the cumulative distribution function, must meet two conditions. One is that no matter how the pixel mapping, the original size of the relationship can not be changed [14]. This ensures that the image contrast is increased, and the dark area remains unchanged. The other is that the range of the pixel mapping function should be within the scope of the original, cannot cross the border. The mapping method is: i Pi P( rk) ( i 0,1,..., M 1) (9) k 0 The above P i is a new gray level; M is all gray levels for the picture. In some cases, the equalization cannot achieve the Fig.7 shows the original histogram and the equalized histogram of the image. You can see the picture of the original histogram waveform is mainly on the left, the image darker, so the histogram equalization is used to adjust brightness. However, after the equalization, image histogram occupies the entire field of gray, making gray distributing uniform. That helps to get clear details of image, improving image quality. Fig. 7 Histogram of image. 4 Comparison of results This system is sensitive to the quality of image. We choice some occluded and dark images, the correct ratio of recognition can be seen from the Table 1. Table 1 Results of the face recognition. Parameters Correct ratio Glasses Occlusion Darkness Face Recognition 9% 45% 79% 5 Conclusion In this paper, we introduce the face recognition attendance system using PCA algorithm. It can be used in many fields, such as company entrance guard system and attendance records etc. Such a system just needs a camera, costs little. This system consists of a database of a set of facial patterns for each individual. It realized the face information registration, face recognition, attendance
5 1 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.6, No., Jun. 016 sign in/sign out, administrator management function. In making graphic user interface limits the input mode of some important information, this to a certain extent, ensure the completeness of data. The proposed system is a generic application design to automate and shorten the time. Compared with other systems, cameras and computers are enough, other specialized hardware is not needed. By using the histogram equalization algorithm, the impact of light can be reduced, so the recognition rate will be improved. An open problem is still precision and efficiency of the algorithm. At the time of image acquisition, lack of acquisition faces different expressions of the details, Or the light is too dark, background clutter that unable to recognize faces correctly. So future work will be focused on design efficient algorithm on facial expression to enhance the recognition ratio. References [1] Kirby M, Sirovich L. Application of the karhunen-loeve procedure for the characterization of human faces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 1(1): [] Turk M, Pentland A. Eigenfaces for recognition [J]. Journal of cognitive Neuroscience, 1991, 3(1): [3] Viola P, Jones M J. Robust real-time object detection [J]. International Journal of Computer Vision, 004, 57(): [4] Ruprah T S. Face recognition based on pca algorithm [J]. Special Issue of International Journal of Computer Science & Informatics, 01, (1): 1-5. [5] Navaz A S S, Sri T D, Mazumder P. Face recognition using principal component analysis and neural networks [J]. International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC), 013, 3(1): [6] Gross R, Jie Y, Waibel A. Face recognition in a meeting room [J]. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 000: [7] Lin S H. An introduction to face recognition technology [J]. Informing Science Special Issue on Multimedia Information Technologies, 000, 3(1): 1-7. [8] Proyecto Fin de Carrera. Face Recognition Algorithms. [EB/OL]. [ ]. hppt:// eus/ccwintoc/uploads/eb/pfc-ionmarques.pdf. [9] Dalal J, Meena M S, Singh P. A facial recognition technique using principal compent analysis [J]. International Journal of Engineering Research, 015, 3(5): [10] Xiang X G, Yang J, Chen Q P. Color face recognition by PCA-like approach [J]. Neurocomputing, 015, 15: [11] Introna L D, Nissenbaum H. Facial recognition technology. a survey of policy and implementation issues [EB/OL]. [ ]. projects/nissenbaum/papers/facial_recognition_report.pdf. [1] Lu X G. Image analysis for face recognition [EB/OL]. [ ]. general/imana4facrcg_lu.pdf. [13] Balcoh N K, Yousaf M H, Ahmad M,et al. Algorithm for efficient attendance management: face recognition based approach [J]. International Journal of Computer Science Issues, 010, 9(4): [14] Patel U A, Swaminarayan P R. Development of a student attendance management system using REID and face rexognition [J]. International Journal of Advance Research in Computer Science and Management Studies, 014, (8): Li Yanling is born in 1980 and her main research fields are artificial intelligence and image processing. Chen Yisong is an associate professor and got his doctor's degree. His main research fields are image processing. Wang Guoping is a professor and got his doctor's degree. His main research fields are computer graphics and human-computer interaction.
Face recognition based on improved BP neural network
Face recognition based on improved BP neural network Gaili Yue, Lei Lu a, College of Electrical and Control Engineering, Xi an University of Science and Technology, Xi an 710043, China Abstract. In order
More informationFace detection and recognition. Many slides adapted from K. Grauman and D. Lowe
Face detection and recognition Many slides adapted from K. Grauman and D. Lowe Face detection and recognition Detection Recognition Sally History Early face recognition systems: based on features and distances
More informationAutomatic Attendance System Based On Face Recognition
Automatic Attendance System Based On Face Recognition Sujay Patole 1, Yatin Vispute 2 B.E Student, Department of Electronics and Telecommunication, PVG s COET, Shivadarshan, Pune, India 1 B.E Student,
More informationAn 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 informationAlgorithm for Efficient Attendance Management: Face Recognition based approach
www.ijcsi.org 146 Algorithm for Efficient Attendance Management: Face Recognition based approach Naveed Khan Balcoh, M. Haroon Yousaf, Waqar Ahmad and M. Iram Baig Abstract Students attendance in the classroom
More informationHaresh 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 informationRecognition 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 informationLinear 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 informationFace Recognition Technology Based On Image Processing Chen Xin, Yajuan Li, Zhimin Tian
4th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2016) Face Recognition Technology Based On Image Processing Chen Xin, Yajuan Li, Zhimin Tian Hebei Engineering and
More informationA Survey on Feature Extraction Techniques for Palmprint Identification
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 03 Issue. 12 A Survey on Feature Extraction Techniques for Palmprint Identification Sincy John 1, Kumudha Raimond 2 1
More informationFace 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 informationFace Recognition Using SIFT- PCA Feature Extraction and SVM Classifier
IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 5, Issue 2, Ver. II (Mar. - Apr. 2015), PP 31-35 e-issn: 2319 4200, p-issn No. : 2319 4197 www.iosrjournals.org Face Recognition Using SIFT-
More informationFace 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 informationPerformance Evaluation of PCA and LDA for Face Recognition
Performance Evaluation of PCA and LDA for Face Recognition S. K. Hese, M. R. Banwaskar Department of Electronics & Telecommunication, MGM s College of Engineering Nanded Near Airport, Nanded, Maharashtra,
More informationFace 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 informationImage 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 informationCHAPTER 3 PRINCIPAL COMPONENT ANALYSIS AND FISHER LINEAR DISCRIMINANT ANALYSIS
38 CHAPTER 3 PRINCIPAL COMPONENT ANALYSIS AND FISHER LINEAR DISCRIMINANT ANALYSIS 3.1 PRINCIPAL COMPONENT ANALYSIS (PCA) 3.1.1 Introduction In the previous chapter, a brief literature review on conventional
More informationDetermination of 3-D Image Viewpoint Using Modified Nearest Feature Line Method in Its Eigenspace Domain
Determination of 3-D Image Viewpoint Using Modified Nearest Feature Line Method in Its Eigenspace Domain LINA +, BENYAMIN KUSUMOPUTRO ++ + Faculty of Information Technology Tarumanagara University Jl.
More informationFace detection and recognition. Detection Recognition Sally
Face detection and recognition Detection Recognition Sally Face detection & recognition Viola & Jones detector Available in open CV Face recognition Eigenfaces for face recognition Metric learning identification
More informationFace Recognition using Principle Component Analysis, Eigenface and Neural Network
Face Recognition using Principle Component Analysis, Eigenface and Neural Network Mayank Agarwal Student Member IEEE Noida,India mayank.agarwal@ieee.org Nikunj Jain Student Noida,India nikunj262@gmail.com
More informationA Matlab based Face Recognition GUI system Using Principal Component Analysis and Artificial Neural Network
A Matlab based Face Recognition GUI system Using Principal Component Analysis and Artificial Neural Network Achala Khandelwal 1 and Jaya Sharma 2 1,2 Asst Prof Department of Electrical Engineering, Shri
More informationFast Face Detection Assisted with Skin Color Detection
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 4, Ver. II (Jul.-Aug. 2016), PP 70-76 www.iosrjournals.org Fast Face Detection Assisted with Skin Color
More informationComparison of Different Face Recognition Algorithms
Comparison of Different Face Recognition Algorithms Pavan Pratap Chauhan 1, Vishal Kumar Lath 2 and Mr. Praveen Rai 3 1,2,3 Computer Science and Engineering, IIMT College of Engineering(Greater Noida),
More informationFace Recognition Based on LDA and Improved Pairwise-Constrained Multiple Metric Learning Method
Journal of Information Hiding and Multimedia Signal Processing c 2016 ISSN 2073-4212 Ubiquitous International Volume 7, Number 5, September 2016 Face Recognition ased on LDA and Improved Pairwise-Constrained
More informationSemi-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 informationIllumination 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 informationFace Recognition System Using PCA
Face Recognition System Using PCA M.V.N.R. Pavan Kumar 1, Shaikh Arshad A. 2, Katwate Dhananjay P. 3,Jamdar Rohit N. 4 Department of Electronics and Telecommunication Engineering 1,2,3,4, LNBCIET, Satara-415020
More informationPerformance Evaluation of the Eigenface Algorithm on Plain-Feature Images in Comparison with Those of Distinct Features
American Journal of Signal Processing 2015, 5(2): 32-39 DOI: 10.5923/j.ajsp.20150502.02 Performance Evaluation of the Eigenface Algorithm on Plain-Feature Images in Comparison with Those of Distinct Features
More informationRobust 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 informationDr. K. Nagabhushan Raju Professor, Dept. of Instrumentation Sri Krishnadevaraya University, Anantapuramu, Andhra Pradesh, India
Volume 6, Issue 10, October 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Design and
More informationPCA and KPCA algorithms for Face Recognition A Survey
PCA and KPCA algorithms for Face Recognition A Survey Surabhi M. Dhokai 1, Vaishali B.Vala 2,Vatsal H. Shah 3 1 Department of Information Technology, BVM Engineering College, surabhidhokai@gmail.com 2
More informationInternational Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 8, March 2013)
Face Recognition using ICA for Biometric Security System Meenakshi A.D. Abstract An amount of current face recognition procedures use face representations originate by unsupervised statistical approaches.
More informationResearch Article Research on Face Recognition Based on Embedded System
Mathematical Problems in Engineering Volume 2013, Article ID 519074, 6 pages http://dx.doi.org/10.1155/2013/519074 Research Article Research on ace Recognition Based on Embedded System Hong Zhao, Xi-Jun
More informationMobile 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 informationApplications 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 informationImage 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 informationRecognition of Human Body Movements Trajectory Based on the Three-dimensional Depth Data
Preprints of the 19th World Congress The International Federation of Automatic Control Recognition of Human Body s Trajectory Based on the Three-dimensional Depth Data Zheng Chang Qing Shen Xiaojuan Ban
More informationFace Recognition for Different Facial Expressions Using Principal Component analysis
Face Recognition for Different Facial Expressions Using Principal Component analysis ASHISH SHRIVASTAVA *, SHEETESH SAD # # Department of Electronics & Communications, CIIT, Indore Dewas Bypass Road, Arandiya
More informationAn Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image
International Journal of Computer Science Issues, Vol. 2, 2009 ISSN (Online): 694-0784 ISSN (Print): 694-084 49 An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image Nageshkumar.M,
More informationAlgorithm research of 3D point cloud registration based on iterative closest point 1
Acta Technica 62, No. 3B/2017, 189 196 c 2017 Institute of Thermomechanics CAS, v.v.i. Algorithm research of 3D point cloud registration based on iterative closest point 1 Qian Gao 2, Yujian Wang 2,3,
More informationFACE RECOGNITION BASED ON GENDER USING A MODIFIED METHOD OF 2D-LINEAR DISCRIMINANT ANALYSIS
FACE RECOGNITION BASED ON GENDER USING A MODIFIED METHOD OF 2D-LINEAR DISCRIMINANT ANALYSIS 1 Fitri Damayanti, 2 Wahyudi Setiawan, 3 Sri Herawati, 4 Aeri Rachmad 1,2,3,4 Faculty of Engineering, University
More informationA Study on Similarity Computations in Template Matching Technique for Identity Verification
A Study on Similarity Computations in Template Matching Technique for Identity Verification Lam, S. K., Yeong, C. Y., Yew, C. T., Chai, W. S., Suandi, S. A. Intelligent Biometric Group, School of Electrical
More informationImage-Based Face Recognition using Global Features
Image-Based Face Recognition using Global Features Xiaoyin xu Research Centre for Integrated Microsystems Electrical and Computer Engineering University of Windsor Supervisors: Dr. Ahmadi May 13, 2005
More informationPrincipal Component Analysis (PCA) is a most practicable. statistical technique. Its application plays a major role in many
CHAPTER 3 PRINCIPAL COMPONENT ANALYSIS ON EIGENFACES 2D AND 3D MODEL 3.1 INTRODUCTION Principal Component Analysis (PCA) is a most practicable statistical technique. Its application plays a major role
More informationDr. 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 informationA New Multi Fractal Dimension Method for Face Recognition with Fewer Features under Expression Variations
A New Multi Fractal Dimension Method for Face Recognition with Fewer Features under Expression Variations Maksud Ahamad Assistant Professor, Computer Science & Engineering Department, Ideal Institute of
More informationGPU Based Face Recognition System for Authentication
GPU Based Face Recognition System for Authentication Bhumika Agrawal, Chelsi Gupta, Meghna Mandloi, Divya Dwivedi, Jayesh Surana Information Technology, SVITS Gram Baroli, Sanwer road, Indore, MP, India
More informationPrincipal 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 informationColor Space Projection, Feature Fusion and Concurrent Neural Modules for Biometric Image Recognition
Proceedings of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, 2006 286 Color Space Projection, Fusion and Concurrent Neural
More informationRecognition: Face Recognition. Linda Shapiro EE/CSE 576
Recognition: Face Recognition Linda Shapiro EE/CSE 576 1 Face recognition: once you ve detected and cropped a face, try to recognize it Detection Recognition Sally 2 Face recognition: overview Typical
More informationBiometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong)
Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong) References: [1] http://homepages.inf.ed.ac.uk/rbf/hipr2/index.htm [2] http://www.cs.wisc.edu/~dyer/cs540/notes/vision.html
More informationComputerized Attendance System Using Face Recognition
Computerized Attendance System Using Face Recognition Prof. S.D.Jadhav 1, Rajratna Nikam 2, Suraj Salunke 3, Prathamesh Shevgan 4, Saurabh Utekar 5 1Professor, Dept. of EXTC Engineering, Bharati Vidyapeeth
More informationA Study on Different Challenges in Facial Recognition Methods
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 6, June 2015, pg.521
More informationLinear 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 informationWaleed Pervaiz CSE 352
Waleed Pervaiz CSE 352 Computer Vision is the technology that enables machines to see and obtain information from digital images. It is seen as an integral part of AI in fields such as pattern recognition
More informationMulti-Modal Human Verification Using Face and Speech
22 Multi-Modal Human Verification Using Face and Speech Changhan Park 1 and Joonki Paik 2 1 Advanced Technology R&D Center, Samsung Thales Co., Ltd., 2 Graduate School of Advanced Imaging Science, Multimedia,
More informationNOWADAYS, there are many human jobs that can. Face Recognition Performance in Facing Pose Variation
CommIT (Communication & Information Technology) Journal 11(1), 1 7, 2017 Face Recognition Performance in Facing Pose Variation Alexander A. S. Gunawan 1 and Reza A. Prasetyo 2 1,2 School of Computer Science,
More informationRestricted Nearest Feature Line with Ellipse for Face Recognition
Journal of Information Hiding and Multimedia Signal Processing c 2012 ISSN 2073-4212 Ubiquitous International Volume 3, Number 3, July 2012 Restricted Nearest Feature Line with Ellipse for Face Recognition
More informationMultidirectional 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 informationMingle Face Detection using Adaptive Thresholding and Hybrid Median Filter
Mingle Face Detection using Adaptive Thresholding and Hybrid Median Filter Amandeep Kaur Department of Computer Science and Engg Guru Nanak Dev University Amritsar, India-143005 ABSTRACT Face detection
More informationAPPLICATION 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 informationFace Recognition Using Principal Component Analysis in MATLAB
International Journal of Scientific Research in Computer Science and Engineering Research Paper Volume-3, Issue-1 ISSN: 2320-7639 Face Recognition Using Principal Component Analysis in MATLAB Prabhjot
More informationDisguised 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 informationGeneric Face Alignment Using an Improved Active Shape Model
Generic Face Alignment Using an Improved Active Shape Model Liting Wang, Xiaoqing Ding, Chi Fang Electronic Engineering Department, Tsinghua University, Beijing, China {wanglt, dxq, fangchi} @ocrserv.ee.tsinghua.edu.cn
More informationA Survey of Various Face Detection Methods
A Survey of Various Face Detection Methods 1 Deepali G. Ganakwar, 2 Dr.Vipulsangram K. Kadam 1 Research Student, 2 Professor 1 Department of Engineering and technology 1 Dr. Babasaheb Ambedkar Marathwada
More informationImage enhancement for face recognition using color segmentation and Edge detection algorithm
Image enhancement for face recognition using color segmentation and Edge detection algorithm 1 Dr. K Perumal and 2 N Saravana Perumal 1 Computer Centre, Madurai Kamaraj University, Madurai-625021, Tamilnadu,
More informationEigenfaces and Fisherfaces A comparison of face detection techniques. Abstract. Pradyumna Desale SCPD, NVIDIA
Eigenfaces and Fisherfaces A comparison of face detection techniques Pradyumna Desale SCPD, NVIDIA pdesale@nvidia.com Angelica Perez Stanford University pereza77@stanford.edu Abstract In this project we
More informationLast week. Multi-Frame Structure from Motion: Multi-View Stereo. Unknown camera viewpoints
Last week Multi-Frame Structure from Motion: Multi-View Stereo Unknown camera viewpoints Last week PCA Today Recognition Today Recognition Recognition problems What is it? Object detection Who is it? Recognizing
More informationFacial Feature Extraction Based On FPD and GLCM Algorithms
Facial Feature Extraction Based On FPD and GLCM Algorithms Dr. S. Vijayarani 1, S. Priyatharsini 2 Assistant Professor, Department of Computer Science, School of Computer Science and Engineering, Bharathiar
More informationAn algorithm of lips secondary positioning and feature extraction based on YCbCr color space SHEN Xian-geng 1, WU Wei 2
International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 015) An algorithm of lips secondary positioning and feature extraction based on YCbCr color space SHEN Xian-geng
More informationAAM Based Facial Feature Tracking with Kinect
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 15, No 3 Sofia 2015 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.1515/cait-2015-0046 AAM Based Facial Feature Tracking
More informationEnhancing Performance of Face Recognition System Using Independent Component Analysis
Enhancing Performance of Face Recognition System Using Independent Component Analysis Dipti Rane 1, Prof. Uday Bhave 2, and Asst Prof. Manimala Mahato 3 Student, Computer Science, Shah and Anchor Kuttchi
More informationDigital Information Facial Recognition Based on PCA and Its Improved Algorithm
Digital Information Facial Recognition Based on PCA and Its Improved Algorithm Hai-feng Zhu School of Electronics and Information Nantong University Nantong City Jiangsu Province China 226019 bauhauscg@163.com
More informationKeyless Car Entry Authentication System Based on A Novel Face-Recognition Structure
Keyless Car Entry Authentication System Based on A Novel Face-Recognition Structure I.Amulya M.Tech VLSI System Design, AITS, Rajampet, Kadapa (DT) Mr. K. Sreenivasa Rao, Associate Professor, Dept: ECE,
More informationFuzzy Bidirectional Weighted Sum for Face Recognition
Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 2014, 6, 447-452 447 Fuzzy Bidirectional Weighted Sum for Face Recognition Open Access Pengli Lu
More informationPerformance Evaluation of Optimised PCA and Projection Combined PCA methods in Facial Images
Journal of Computations & Modelling, vol.2, no.3, 2012, 17-29 ISSN: 1792-7625 (print), 1792-8850 (online) Scienpress Ltd, 2012 Performance Evaluation of Optimised PCA and Projection Combined PCA methods
More informationActive Appearance Models
Active Appearance Models Edwards, Taylor, and Cootes Presented by Bryan Russell Overview Overview of Appearance Models Combined Appearance Models Active Appearance Model Search Results Constrained Active
More informationVideo Image Based Multimodal Face Recognition System
Paper Number 14 Video Image Based Multimodal Face Recognition System Craig Belcher, Matt Terry, Sophia Vinci-Booher, Yingzi Du Indiana Univ.-Purdue Univ. Indianapolis Email: {csbelche, macterry, svincibo,
More informationAn 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 informationComputer Science Faculty, Bandar Lampung University, Bandar Lampung, Indonesia
Application Object Detection Using Histogram of Oriented Gradient For Artificial Intelegence System Module of Nao Robot (Control System Laboratory (LSKK) Bandung Institute of Technology) A K Saputra 1.,
More informationInternational 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 informationAN APPROACH OF SEMIAUTOMATED ROAD EXTRACTION FROM AERIAL IMAGE BASED ON TEMPLATE MATCHING AND NEURAL NETWORK
AN APPROACH OF SEMIAUTOMATED ROAD EXTRACTION FROM AERIAL IMAGE BASED ON TEMPLATE MATCHING AND NEURAL NETWORK Xiangyun HU, Zuxun ZHANG, Jianqing ZHANG Wuhan Technique University of Surveying and Mapping,
More informationLecture 4 Face Detection and Classification. Lin ZHANG, PhD School of Software Engineering Tongji University Spring 2018
Lecture 4 Face Detection and Classification Lin ZHANG, PhD School of Software Engineering Tongji University Spring 2018 Any faces contained in the image? Who are they? Outline Overview Face detection Introduction
More informationFace and Facial Expression Detection Using Viola-Jones and PCA Algorithm
Face and Facial Expression Detection Using Viola-Jones and PCA Algorithm MandaVema Reddy M.Tech (Computer Science) Mailmv999@gmail.com Abstract Facial expression is a prominent posture beneath the skin
More informationFace Recognition using Eigenfaces SMAI Course Project
Face Recognition using Eigenfaces SMAI Course Project Satarupa Guha IIIT Hyderabad 201307566 satarupa.guha@research.iiit.ac.in Ayushi Dalmia IIIT Hyderabad 201307565 ayushi.dalmia@research.iiit.ac.in Abstract
More informationComparisonofDifferentAlgorithmforFaceRecognition
Global Journal of Computer Science and Technology Graphics & Vision Volume 13 Issue 9 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc.
More informationComparative Analysis of Face Recognition Algorithms for Medical Application
Comparative Analysis of Face Recognition Algorithms for Medical Application Suganya S and Menaka D Department of Information and Communication Engineering Sri Venkateswara College of Engineering Irungattukottai,
More informationFace Detection using Hierarchical SVM
Face Detection using Hierarchical SVM ECE 795 Pattern Recognition Christos Kyrkou Fall Semester 2010 1. Introduction Face detection in video is the process of detecting and classifying small images extracted
More informationHUMAN TRACKING SYSTEM
HUMAN TRACKING SYSTEM Kavita Vilas Wagh* *PG Student, Electronics & Telecommunication Department, Vivekanand Institute of Technology, Mumbai, India waghkav@gmail.com Dr. R.K. Kulkarni** **Professor, Electronics
More informationFACE 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 informationGender Classification Technique Based on Facial Features using Neural Network
Gender Classification Technique Based on Facial Features using Neural Network Anushri Jaswante Dr. Asif Ullah Khan Dr. Bhupesh Gour Computer Science & Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya,
More informationDiagonal Principal Component Analysis for Face Recognition
Diagonal Principal Component nalysis for Face Recognition Daoqiang Zhang,2, Zhi-Hua Zhou * and Songcan Chen 2 National Laboratory for Novel Software echnology Nanjing University, Nanjing 20093, China 2
More informationSchool of Computer and Communication, Lanzhou University of Technology, Gansu, Lanzhou,730050,P.R. China
Send Orders for Reprints to reprints@benthamscienceae The Open Automation and Control Systems Journal, 2015, 7, 253-258 253 Open Access An Adaptive Neighborhood Choosing of the Local Sensitive Discriminant
More informationTraffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers
Traffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers A. Salhi, B. Minaoui, M. Fakir, H. Chakib, H. Grimech Faculty of science and Technology Sultan Moulay Slimane
More informationHybrid 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 informationResearch on Evaluation Method of Video Stabilization
International Conference on Advanced Material Science and Environmental Engineering (AMSEE 216) Research on Evaluation Method of Video Stabilization Bin Chen, Jianjun Zhao and i Wang Weapon Science and
More informationDISTANCE MAPS: A ROBUST ILLUMINATION PREPROCESSING FOR ACTIVE APPEARANCE MODELS
DISTANCE MAPS: A ROBUST ILLUMINATION PREPROCESSING FOR ACTIVE APPEARANCE MODELS Sylvain Le Gallou*, Gaspard Breton*, Christophe Garcia*, Renaud Séguier** * France Telecom R&D - TECH/IRIS 4 rue du clos
More informationAn embedded system of Face Recognition based on ARM and HMM
An embedded system of Face Recognition based on ARM and HMM Yanbin Sun 1,2, Lun Xie 1, Zhiliang Wang 1,Yi An 2 1 Department of Electronic Information Engineering, School of Information Engineering, University
More informationProject Report for EE7700
Project Report for EE7700 Name: Jing Chen, Shaoming Chen Student ID: 89-507-3494, 89-295-9668 Face Tracking 1. Objective of the study Given a video, this semester project aims at implementing algorithms
More informationPublished by: PIONEER RESEARCH & DEVELOPMENT GROUP(www.prdg.org) 1
FACE RECOGNITION USING PRINCIPLE COMPONENT ANALYSIS (PCA) ALGORITHM P.Priyanka 1, Dorairaj Sukanya 2 and V.Sumathy 3 1,2,3 Department of Computer Science and Engineering, Kingston Engineering College,
More information