Gait Recognition System Using Gabor Wavelet and Active Gait Differential Image
AbstractGait is a behavioural biometric process that serves to identify persons using their walking style. It is un-obstructive, not easy to conceal and offers distance recognition. Various approaches have been employed to improve the performance and accuracy of gait biometric systems but the performance is yet to measure up to that of other biometric recognition systems. In this work, Gabor wavelets were used to extract Active Gait Differential Image (AGDI) features, while Principal Component Analysis (PCA) was used for feature dimensionality reduction. The classification was performed using Support Vector Machine (SVM) and silhouette images from Chinese Academy of Science Institute of Automation (CASIA) gait dataset was used for testing. The performance was evaluated using accuracy, equal error rate, false acceptance rate and false rejection rate and it gave 99.19%, 1%, 0%, and 2% respectively for the metrics used.
. Liang, W., Tieniu, T., Huazhong, Ning., and Weiming H., (2003) “Silhouette Analysis Based- Gait Recognition for Human Identification”. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, (12), pp. 1505-1518.
. Zhuowen, L.v., Xianglei, X., Kejun, W., and Donghai, G., (2015), “Class Energy Image Analysis for Video Sensor-Based Gait Recognition. Sensors, 2015, (15), pp. 932-964.
. Chiung, C.H., Eswaran, C., and June, Y.L., (2010), “An unobstructive Android person verification using Accelerometer based Gait”. Proceedings of 10th International conference on advances in mobile computing and multimedia. pp. 271-274.
. Johansson, G. (1976), “Spatio-temporal differentiation and integration in visual motion perception”. Psychological Research, vol.38, pp 379-393.
. Huang, P.S., Harris, C. J., and Nixon, M.S., “Human gait Recognition in Canonical space using temporal template” IEEE Proceedings-Vision image signal process, Vol. 146, no. 2
. Michael, O., (2005) “Application of continuous wave radar for human gait recognition”. The Proceedings of the SPIE, Volume 5809, pp. 538-548.
. Bobick, A.F. and Johnson, A.Y., "Gait recognition using static, activity-specific parameters," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp. I-423-I-430, vol.1.
. BenAbdelkader, C., Cutler. R and Davis. L., (2002) “Stride and cadence as a biometric in automatic person identification and verification”. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002. pp. 372- 377.
. Murat, E.K., (2006) “Human Identification Using Gait”. Computer Vision Lab.” Turkish Journal of Electrical Engineering & Computer Sciences, 14, (2), pp267-291.
. Goffredo, M., Carter, J.N., and Nixon, M.S., (2008) “Front-view Gait Recognition”. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS, Arlington, USA, pp. 1-6.
. Khalid B., Tao X., Shaogang G. (2008) “Feature selection on Gait Energy Image for Human Identification”. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Las Vegas, USA, April 2008, pp. 985-988.
. Chen C., Liang J., Zhao H., Hu H., Tian J. (2003): Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition. Lett. 30(11), 977–984
. Hayder, A., Jamal D., Chekima, A., and Ervin, G.M., (2011) “Gait Recognition using Gait Energy Image”. International Journal of Sigal Processing, Image Processing and Pattern Recognition, 4 (3), pp. 141-152.
. Sabesan, S., Daniel, C., Simon, D., Sridha, S., and Clinton, F., (2011), “Gait Energy Volumes and Frontal Gait Recognition using Depth Images”. 2011 International Joint Conference on Biometrics (IJCB), Washington DC, USA, October 2011, pp.1-6.
. Yu, S., Tan, D. and Tan, T., 2006, August. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on 4, pp. 441-444, IEEE.
. Chen, J., and Liu, L., (2014), “Active Gait Differential Image Based Human Recognition”.TheScientific World Journal 2014, pp.1-8.
. Arora, P., Srivastava, S. and Singhal, S., 2016. Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition. International Journal of Rough Sets and Data Analysis (IJRSDA), 3(2), pp.45-64.
. Iman. M.B., and Jan, N. (2015), “ Multiview Gait Recognition Using Enhanced Gait Energy Image and Radon Transform technique”, Asian journal of applied science, 8 (2), pp 138-148.
. Xiaoxiang, L., and Youbing, C., (2013), “Gait Recognition based on Structural Gait Energy Image”, Journal of computational information systems (2013) pp 121-126.
. Khalid B., Tao X., Shaogang G. (2009) “Gait Recognition using Gait Entropy Image”, Department of computer science, 3rd International Conference on Crime Detection and Prevention (ICDP 2009), London, UK, Dec. 2009, pp. 1-6.
. Lili, L., Yilong Y., and Wei Q., (2011) “Gait recognition based on outermost contour”. International Journal of Computational Intelligence Systems,4 (5), pp. 1090-1099.
. CASIA Gait Database. 2009, http://www.sinobiometrics.com