This paper represents a method to recognize the human walking individuals by their gait using Principal Component Analysis (PCA). Human Gait is used as principal identifying feature to generate the unique gait sequence for each individual. Gait is an important biometric feature which delineates the way of locomotion. The generation of binary silhouette frames of walking subjects is the initial step in this method. Some distinguishable gait features, viz., centroid, aspect ratio, orientation, height and width are extracted from the silhouette frames to acquire feature vectors. Then, the PCA is employed over the generated feature vectors to condense the information contained and produces the principal components which are used as gait sequences or signatures to represent each walking individuals. Finally the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance. A classification rate of 93% is achieved from the proposed human recognition method which is tested using CASIA dataset.

PCA, Gait, Silhouette, Feature Vector Human Recognition, CASIA dataset

[1]. Dong Ming, Cong Zhang, Yanru Bai, Baikun Wan, Yong Hu and KDK Luk, “Gait Recognition Based on Multiple Views Fusion of Wavelet Descriptor and Human Skeleton Model”, IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems, pp. 246-249, 2009.
[2]. M. Nixon, J.D.Cunado, P. Huang and S. Stevenage, “Automatic Gait Recognition”, Biometrics Personal Identification in Networked Soc. A. Jain, ed., 1999.
[3]. S. Saharia, S.N.N. Pandit, M. Borah, “Orthogonal Moments and principal component analysis in Image Reconstruction and Pattern Recognition” in Proceeding of the International Symposium on Intelligent Robotic Systems, January 10-12, 1998, Bangalore.
[4]. G. Johansson, Visual motion perception. Scientific American, (232):76–88, 1975
[5]. Jianqiang Huang, Zhengming Yi, Xiaoying Wang and Huan Wu, “Gait recognition System based on (2D) 2 PCA and HMM”, Proc. SPIE10033, Eight International Conference on Digitial Image Processing (ICDIP 2016), 1003310, August 29, 2016; doi: 10.1117/12.224484
[6]. Sa-Mun KIM, Dae-Jong Lee, HO-Hyun Lee, Myung-Geun Chun, “Person Recognition Using Gait and Face features on thermal images”, The Transactions of Korean Institute of Electrical Engineering, vol. 65(2), pp. 130-135, 2016.
[7]. Amamoto and A. Fujii, “Detecting obstructions and tracking moving objectsby image processing technique”, Electronics and Communications in Japan, Part 3, vol. 82, no. 11, pp. 28–37, 1999.
[8]. E. J. Marey, Analysecin´ematique de la marche [chronophotograph]. C.R.S ´eances Acad. Sci. 98:1218- 1225, 1884
[9]. L. Lee and W.E.L. Grimson, “Gait analysis for recognition and classification”, Fifth IEEE Conference on Face and Gesture Recognition, 155-161, 2002.
[10]. R. Mukundan and K. R. Ramakrishnan, “Moment Functions in Image Analysis Theory and Applications”, Moment functions in image analysis. World Scientific, 1998.
[11]. L. Wang, W.M. Hu, and T.N. Tan, “Recent Developments in Human Motion Analysis” Pattern Recognition, vol. 36, no. 3, pp. 585-601, 2003.
[12]. Haritaoglu, D. Harwood, and L.S. Davis, “W4: real-time surveillance of people and their activities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809–830, Aug. 2000.
[13]. Troje, N. F. Decomposing biological motion: A framework for analysis and synthesis of Human gait patterns. Journal of Vision, 2, 2002.
[14]. J. H. Yoo, D. Hwang, and M.S. Nixon, “Gender Classification in Human Gait Using Support Vector Machine”, Springer Berlin / Heidelberg, 2005.
[15]. C. Y. Yam and M.S. Nixon, “Model-based Gait Recognition”, Encyclopedia of Biometrics,1082-1088, 2009
[16]. G. Johansson, “Visual perception of biological motion and a model for its analysis”, Perception and Psychophysics 14 (1973) 201–211
[17]. S. NiyogI and E. Adelson, “Analyzing and Recognizing Walking Figures in XYT”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, Wash, USA, pp. 469-474, 1994.
[18]. J. Boyd and J, Little, “Phase in model-free perception of gait”, in Proc. IEEE Workshop Human Motion, 2000.
[19]. C. Y. Yam, M. S. Nixon and J. N. Carter, “Gait recognition by walking and running: A model based approch”, in Proc. 5th Asian Conf. Computerr Vision, Melbourn, Australia, 2002, pp. 1-6.
[20]. Chiraz BenAbdelkader, Ross Cutler and Larry Davis,
“View-Invariant Estimation of Height and Stride for Gait
Recognition”, Biometric Authentication, European Conference on Computer Vision: International. Workshop, vol.4, pp. 155-167, 2002.
[21]. Jinyan Chen, Jiansheng Liu, ”Average Gait Differential Image Based Human Recognition”, The Scientific World Journal, vol. 2014, pp. 1-8, 2014.
[22]. Murat Ekinci, “A New Attempt to Silhouette-Based Gait
Recognition for Human Identification”, Computer Science
Journals, vol. 4013, pp. 443-454, 2006.
[23]. Hu Ng, Hau-Lee Tong, Wooi-Haw Tan and Timothy TzenVun Yap, ”Human Identification Based on Extracted Gait Features”, International Journal on New Computer Architectures and Their Applications, vol. 1, pp. 358-370, 2011.
[24]. Emdad Hossain and Girija Chetty, ”Person Identification in
Surveillance Video Using Gait Biometric cues”, International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1877-1881, 2012.
[25]. Bissacco, A. Chiuso, Y. Ma, S. Soatto, “ Recognition of human gaits”, in Computer Vision and Pattern Recognition, 2001, Volume II., Kauai, HI (2001) 52–57.
[26]. P. Das, D. Das and S. Saharia, “Gait Analysis and Recognition for Human Identification”, in International Journal of Electronics and Applied Research (IJEAR) 2014, 1, 45-54, SSN NO: 2395-0064.
[27]. Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: IEEE Workshop on Motion and Video Computing, Orlando, FL (2002) 22–27.
[28]. Stauffer, W.E.L. Grimson, “Adaptive background mixture models for real-time tracking”, in Computer Vision and Pattern Recognition 99, VolumeII, (1999) 246–252
[29]. M. Garcia, A. Chatterjee, A. Ruina, M. Coleman, “The simplest walking model, “stability, complexity, and scaling”, ASME Journal of Biomechanical Engineering 120 (1998) 281–288.
[30]. http://www.cbsr.ia.ac.cn
[31]. http://www.abi.auckland.ac.nz