Gait Recognition Based Improved Histogram Muzhir Sh. Al-Ani, Isra H. Al-Ani

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1 Gait Recognition Based Improved Histogram Muzhir Sh. Al-Ani, Isra H. Al-Ani ABSTRACT Biometrics is an important and automated method of recognizing persons based on a physiological or behavioral characteristic. Gait recognition biometric technologies becoming an important and highly secure identification and personal verification solutions. In biometric system and especially in gait recognition, one of the challenges that use object extraction is to create silhouette image. In this paper we present an interested method based on histogram of colored image to create silhouette image. It is clear that the best bundle rectangle around the object is obtained by using the green part of the image. The histogram of each color is implemented to decide the range of intensities that represented the background, and then these three silhouettes are intersected to get the final silhouette. Keywords: Gait Recognition, Biometrics Technologies, Secure Identification, Personal Verification and Object Extraction. 1. INTRODUCTION Biometrics approaches are technologies used for measuring and analyzing a person's unique characteristics. There are two types of biometrics: behavioral and physical. Behavioral biometrics systems are generally used for verification while physical biometrics can be used for either identification or verification. A biometric is an aspect that something can be used to verify the identity of an individual. The most common biometric that comes to mind is a finger print. Recent events have brought national interest in quick identification of suspicious individuals. Areas such as airports, parking lots, banks, and bus/subway stations, all have a need for quick detection of threats. However current biometrics such as fingerprints, and face recognition, iris recognition are limited and time consuming. Trying to fingerprint everyone that walks through an airport is not possible. It is probably not even legal. A major advantage of gait recognition is that is it unobtrusive. It can be measured at a distance, without the knowledge or cooperation of the subject, [4,13]. There for studies present new method which It can be measured at a distance, without the knowledge or cooperation of the subject "Gait recognition". Gait Recognition is performed a discrimination by catching the person's walking characteristic from a person's walking image from the camera. It has the advantages which are not in other biometrics mentioned: [5,15] It is un contacting, and a user is not forced special operation for identification. A user is not conscious of being recognized, since identification is performed at the time of the usual walking operation. Identification can he performed from a long distance. It is easy to acquire the data since man usually needs to walk at the time of movement. The gait recognition system has also disadvantaged for example Physical changes, Psychological, Clothing, Stimulants [6]. The major steps in the gait recognition system are: data collection, background subtraction, feature extraction and recognition. One of the most challenges in gait recognition system is background subtraction, in this paper we try to present a method to extract the object by using the histogram technique. 2. HISTOGRAM TECHNIQUE There are many methods using histogram to understand the feature of an image, gray level histogram analysis is a known technique that allows easy and fast segmentation of the regions of interest in an image[3]. The histogram of a digital image with gray levels in the range [0,L-1] is a discrete function h(r k )=n k, where r k is the kth gray level and n k is the number of pixels in the image having gray level r k. It is common practice to normalize a histogram by dividing each of its values by the total number of pixels in the image, denoted by n. Thus, a normalized histogram is given by p(r k )=n k /n, for k=0,1,p, L-1. Loosely speaking, p(r k ) gives an estimate of the probability of occurrence of gray level r k. Note that the sum of all components of a normalized histogram is equal to 1[1]. The histogram is given explicitly by (1) Iff contains exactly J occurrences of gray level k, for each k = 0,..., K - 1. Thus, an algorithm to compute the image histogram involves a simple counting of gray levels, which can be accomplished even as the image is scanned. Every image processing development environment and software library contains basic histogram computation, manipulation, and display routines. The histogram Hf contains no spatial information about f it describes the frequency of the gray levels in f and nothing more. However, this information is still very rich. The histogram supplies an absolute method of determining an image s gray-level distribution. For example, Optical density (OD) is the measure of the transmission of an optical medium for a given wavelength. Higher OD lower transmittance and vice versa. The Average Optical Density(AOD)or Mean Optical Density (MOD) is the basic measure of an image s overall average brightness or gray level. AOD can be computed from the image histogram in (2): The AOD is a useful and simple meter for estimating the center of an image s gray-level distribution. More generally, an image may have a histogram that reveals a poor usage of the available gray-scale range [2]. 674

2 3. BIOMETRIC RECOGNITION Biometrics technologies measure a particular set of a person's vital statistics in order to determine identity. Biometrics in the high technology sector refers to a particular class of identification technologies. These technologies use an individual's unique biological traits to determine one's identity. The traits that are considered include fingerprints, retina and iris patterns, facial characteristics and many more. There are basically two types of biometrics : Behavioral biometric definition: Behavioral biometrics basically measures the characteristics which are acquired naturally over a time. It is generally used for verification, behavioral biometrics include [13,14]: Speaker Recognition - analyzing vocal behavior Signature - analyzing signature dynamics Keystroke - measuring the time spacing of typed words Physical biometric definition: Physical biometrics measures the inherent physical characteristics on an individual. It can be used for either identification or verification, physical biometrics include : Bertillonage - measuring body lengths Fingerprint - analyzing fingertip patterns Facial Recognition - measuring facial characteristics Hand Geometry - measuring the shape of the hand Iris Scan - analyzing features of colored ring of the eye Retinal Scan - analyzing blood vessels in the eye Vascular Patterns - analyzing vein patterns DNA - analyzing genetic makeup 4. GAIT RECOGNITION TECHNIQUE Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait recognition (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research [7]. Human gait recognition system has many advantages as biometric option, such as being an unobtrusive technology, can be captured at a distance, it does not require the consent of the observed individual and it is very difficult to steal, fake or hide[8]. Most gait recognition systems work in the same general way as shown in Figure (1). Firstly, data must be collected from the individual in question. In this step it helps to have the background be as simple as possible to provide the highest level of recognition. Additionally selection of an appropriate viewpoint, one in which the gait is observed from the side, is also important. From here, through a process called background subtraction, the object or gait is separated from the background noise. Next the specific markers of the identification scheme are extracted from the gait data. These are compared with the database in hopes of a positive recognition[4]. Silhouette extraction, namely, segmenting a human body or objects from a background, is usually the first and enabling step for many high-level vision analysis tasks, such as video surveillance, people tracking and activity recognition. One central task in human silhouette extraction is background modeling[9, 14, 15]. Gait video Data Base subtraction Feature extraction Recognition Figure 1: General diagram of gait recognition system 5. RELATED WORKS There are many paper published in this field and some of them are mentioned below: Edward et al. in their paper they proposed a biometric method include recognition biometric techniques. This paper shows a gait recognition system with feature subtraction on a bundle rectangle drawn over the observed person. Statistical results within a database of 500 videos are shown [9]. Fernando in his paper summarized three algorithms used for the analysis of gray level histogram. His work represented multi model histograms that 675

3 improving the time efficiency of most widely used method (Otsu's). Finally he investigates the potentials of these Ashwani et al. presented a fast and robust method for moving object tracking directly in the compressed domain using features available in MPEG videos. DCT domain background subtraction in Y plane is used to locate candidate objects in subsequent I-frames. DCT domain histogram matching using Cb and Cr planes and motion vectors are used to select the target object from the set of candidate objects. The target object position is finally interpolated in the predicted frames to obtain a smooth tracking a cross GOPs [10]. Murat et al. proposed an improved method for gait recognition. Binaries silhouette of a motion object is firstly represented by four1-d signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Then he employed Fourier Transforms to feature extraction and SVM for recognition [11]. Bineng et al. proposed a local histogram of figure/ground segmentations, for background subtraction (BGS) in dynamic scenes. he represent each pixel as a local histogram of figure/ground segmentations. The background model of each pixel is constructed as a group of weighted adaptive local histograms, which describe the structure properties of the surrounding region. This can be used to build background models for scenes. Moreover, the correlation of image variations at neighboring pixels is explicitly utilized to achieve robust detection performance [12]. techniques for scalar quantifiers design (because Otsu method is a clustering technique) [3]. 6. IMPLEMENTED ALGORITHM The implemented algorithm consists of four steps which are video processing, preprocess to each frame, apply histogram for each color, and finally object extraction. These steps explained in figure(2) in which shows the main steps of the proposed system and these steps are explained below: The video processing starts with segmenting the video into frames and select the adequate number of frames, the time span between each selected frame and the one next to it must be equal. The following processing are applied on each selected frame: Apply median filter; sharpening and apply histogram stretching to colored image, Build three histogram model (one for each color of the image Red, Green, and Blue) and Use the Green histogram to create the bundle rectangle around the object, (using the green histogram because we notice that the contrast between close intensities is clearer in this wave). Use the three histograms to segment the object from the background. (Detecting the range that represents the object by determines the threshold that represents the beginning and the end of the background); then by intersecting the three silhouettes the final silhouette will be obtained. Video processing Preprocess each frame Apply histogram for each color Object extraction Figure 2: block diagram of proposed system 7. RESULT AND ANALYSIS After the proposed algorithm is implemented, different types of videos are used to verify the implemented system. Some of these videos are shown in figure (3) and figure (4) that illustrated clearly the original RGB color dark image and bright image respectively. Part (a) shows the original image and bundle rectangle around the object in which the dense pixels of the cycle are concentrated on the range (75-230). Part (b) shows the histogram of green image and extracted image in which the dense pixels of the cycle are concentrated on the range (50-200). Part (c) shows the histogram of red image and extracted image. Part shows the histogram of blue image and extracted image in which the dense pixels of the cycle are concentrated on the range (70-210).Lastly part (e) shows the final result. Figure 4 represents bright object when the difference between the object and the background is small (Type 2), this illustrates that the blue band and green band are so close to the final result, and the final result would be much better by intersecting the three colors. While Figure(3) represents the dark object, when the difference between the object and the background is large enough (Type 1), the three band approximately similar to each other. As a result it was found that in images of type 2, to create the silhouette, it will be much better to process each color band of the image alone to obtain more accurate results rather than processing the entire image colors as a total package. 676

4 (a) (b) (a) Original video and selected image (b) Green band and its histogram (c) (c) Blue band and its histogram (e) final result Red band and its histogram (e) Figure 3: extracted result from the implemented algorithm Type 1 677

5 (a) Original video and selected image (a) (b) (b) (c) (b) Green band and its histogram (c) (c) Blue band and its histogram (e) final result Red band and its histogram (e) Figure 4: extracted result from the implemented algorithm Type 2 678

6 8. CONCLUSION In this paper an introduction of biometric Gait recognition concept and algorithm are presented. The proposed system is implemented for gait recognition system based on histogram technique. A method for obtaining histograms of the frequency of occurrence of optical densities in mammograms has been implemented. This system is constructed of four major steps: video processing, preprocess to each frame in this step we notes that by using the green image produce more perfect bundle rectangle around the object, apply histogram for each color, and finally object extraction applied by subtract the background by using the histogram. Histogram technique is implemented to each color and then intersecting the three obtained results to get the final silhouette image. In this way an acceptable result is obtained that is more efficient than using the histogram of original colored image. REFERENCES [1] Gonzalez R.C., and Woods R.E., Digital Image Processing, Prentice-Hall of India Private Limited, 2nd ed., 2005 [2] Jerry D. Gibson, Handbook of Image and Video Processing,ACADEMIC PRESS,2000. [3] Wang S.Z. and H. J. Lee, "Detection and recognition of license plate characters with different appearances," Intelligent Transportation Systems, IEEE Proceedings, vol. 2, pp , [4] David C Post, Gait Analysis Review, [5] Kota Iwamoto, Kazuyuki Sonobe, and Naohisa Komatsu, A Gait Recognition Method using HMM,SICE, 2003 [6] Mark Ruane Dawson, M. Sc. Thesis, Gait Recognition, Department of Computing Imperial College of Science, Technology & Medicine,2002 [7] Davrondzhon Gafurov, A Survey of Biometric Gait Recognition: Approaches, Security and Challenges,NIK,2007 [8] Edward Guillen, Daniel Padilla, Adriana Hernandez, and Kenneth Barner, Gait Recognition System: Bundle Rectangle Approach, World Academy of science Engineering and Technology, 2009 [9] Xi Chen, Zhihai He, Derek Anderson, James Keller, and Marjorie Skubic, Adaptive Silhouette Extraction and Human Tracking in Dynamic Environments,IEEE International Conference on Fuzzy Systems, [10] AshwaniAggarwal, SusmitBiswas, Sandeep Singh, ShamikSural, and A.K. Majumdar, Object Tracking Using Subtraction and Motion Estimation in MPEG Videos, Springer-VerlagBerlinHeidelberg, 2006 [11] Murat Ekinci Murat Aykut, Improved gait recognition by multiple projections normalization,springer- Verlag2008 [12] BinengZhong,HongxunYao,Shaohui Liu, and Xiaotong Yuan, Local Histogram of Figure/Ground Segmentations for Dynamic Subtraction,EURASIP Journal on Advances in Signal Processing, 2010 [13] JeffreyE.Boyd, JamesJ.Little, BiometricGaitRecognition, Springer- VerlagBerlinHeidelberg, 2005 [14] Rong Zhang, Christian Vogler, Dimitris Metaxas, Human Gait Recognition, Computer Vision and Pattern Recognition Workshop,2004. [15] Yanmei Chai, Qing Wang, JingpingJia, and Rongchun Zhao, A Novel Human Gait Recognition Method by Segmenting and Extracting the Region Variance Feature,IEEE Computer Society Washington, DC, USA,