Commenced in January 2007
Paper Count: 30761
Person Identification using Gait by Combined Features of Width and Shape of the Binary Silhouette
Abstract:Current image-based individual human recognition methods, such as fingerprints, face, or iris biometric modalities generally require a cooperative subject, views from certain aspects, and physical contact or close proximity. These methods cannot reliably recognize non-cooperating individuals at a distance in the real world under changing environmental conditions. Gait, which concerns recognizing individuals by the way they walk, is a relatively new biometric without these disadvantages. The inherent gait characteristic of an individual makes it irreplaceable and useful in visual surveillance. In this paper, an efficient gait recognition system for human identification by extracting two features namely width vector of the binary silhouette and the MPEG-7-based region-based shape descriptors is proposed. In the proposed method, foreground objects i.e., human and other moving objects are extracted by estimating background information by a Gaussian Mixture Model (GMM) and subsequently, median filtering operation is performed for removing noises in the background subtracted image. A moving target classification algorithm is used to separate human being (i.e., pedestrian) from other foreground objects (viz., vehicles). Shape and boundary information is used in the moving target classification algorithm. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Next, the Principal Component Analysis (PCA) is applied to the selected feature vector to reduce its dimensionality. These extracted feature vectors are used to train an Hidden Markov Model (HMM) for identification of some individuals. The proposed system is evaluated using some gait sequences and the experimental results show the efficacy of the proposed algorithm.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328530Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1561
 H. F. Alan J. Lipton and R. S. Patil, "Moving target classification and tracking from real-time video," Proceedings of Fourth IEEE Workshop on Applications of Computer Vision (WACV-98).
 A. N. R. N. P. C. A. K. R.-C. V. K. Amit Kale, Aravind Sundaresan and R. Chellappa, "Identification of humans using gait," IEEE Transactions on Image Processing, vol. 13, no. 9.
 A. K. R.-C. Aravind Sundaresan and R. Chellappa, "A hidden markov model based framework for recognition of humans from gait sequences," Proceeding of ICIP.
 M. Bober, "Mpeg-7 visual shape descriptors," IEEE Transactions on Circuit and Systems for Video Technology, vol. 11, no. 6.
 H. Z. Changhong Chen, Jimin Liang and H. HU, "Gait recognition using hidden markov model," Lecture Notes on Computer Science.
 P. S. Huang, "Automatic gait recognition via statistical approaches for extended template features," IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 31, no. 5, October 2001.
 L. Lee and W. E. L. Grimson, "Gait analysis for recognition and classification," Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition., 2002.
 W. H. Liang Wang, Tieniu Tan and H. Ning, "Automatic gait recognition based on statistical shape analysis," IEEE Transaction on Image processing, vol. 12, no. 9, September 2003.
 ÔÇöÔÇö, "Silhouette analysis-based gait recognition for human identification," IEEE Transaction on Image processing, vol. 25, no. 12, December 2003.
 D. H. Nikolaos V. Boulgouris and K. N. Plataniotis, "Gait recognition: A challenging signal processing technology for biometric identification," IEEE sinal processing magazine, November 2005.
 M. Piccardi, "Background subtraction techniques: A review," IEEE International Conference on Systems, Man and Cybernetics.
 P. W. Power and J. A. Schoonees, "Understanding background mixture models for foreground segmentation," Proceedings of IEEE International Conference on Image and Vision Computing.
 C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time video tracking," Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 82, pp. 246-252.