Abstract
Human gait, a biometric aimed to recognize individuals by the way they walk has recently come to play an increasingly important role in visual surveillance applications. Most of the existing approaches in this area, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised the performance. In this paper, we have investigated the effect of discarding irrelevant or redundant gait features, by employing Genetic Algorithms (GAs) to select an optimal subset of features, on improving the performance of a gait recognition system. Experimental results on the CASIA dataset demonstrate that the proposed system achieves considerable gait recognition performance.
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References
Yoo, J., Nixon, M.S.: Automated Marker-less Analysis of Human Gait Motion for Recognition and Classification. ETRI Journal 33(2), 259–266 (2011)
Oliveir, L.S., Benahmed, N., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Feature subset selection using genetic algorithms for handwritten digit recognition. In: XIV Brazilian Symposium on Computer Graphics and Image Processing, Florianopolis, Brazil, pp. 362-369 (October 2001)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)
Veres, G., Gordon, L., Carter, J.N., Nixon, M.: What image information is important in silhouette-based gait recognition? In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 776–782 (July 2004)
Begg, K.: A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. Journal of Biomechanics 38, 401–408 (2005)
Sun, Z., Bebis, G., Miller, R.: On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization. IEEE Transactions on Intelligent Transportation Systems 6(2), 125–137 (2005)
Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic Feature Subset Selection for Gender Classification: A Comparison Study. In: Sixth IEEE Workshop on Applications of Computer Vision (WACV 2002), pp. 165–170 (2002)
Yoo, J., Hwang, D., Moon, K., Nixon, M.S.: Automated Human Recognition by Gait using Neural Network. In: First Workshops on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6 (November 2008)
Bobick, A.F., Johnson, A.Y.: Gait recognition using static activity-specific parameters. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, pp. 423–430 ( December 2001)
Tanawongsuwan, R., Bobick, A.F.: Gait recognition from time-normalized joint-angle trajectories in the walking plane. In: IEEE Computer Vision and Pattern Recognition Conference (CVPR 2001), Kauai, Hawaii, pp. 726–731 (December 2001)
Wang, A.H., Liu, J.W.: A gait recognition method based on positioning human body joints. In: International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR 2007), Beijing, pp. 1067–1071 (November 2007)
Jean, F., Albu, A.B., Bergevin, R.: Towards view-invariant gait modeling: Computing view-normalized body part trajectories. Pattern Recognition 42(11), 2936–2949 (2009)
Urtasun, R., Fua, P.: 3D Tracking for Gait Characterization and Recognition. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 17–22 (May 2004)
Zhang, X., Fan, G.: Dual gait generative models for human motion estimation from a single camera. IEEE Transactions on System, Man and Cybernetics, Part B: Cybernetics 40(4), 1034–1049 (2010)
Collins, R.T., Gross, R., Shi, J.: Silhouette-based human identification from body shape and gait. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA, pp. 366–371 (May 2002)
BenAbdelkader, C., Cutler, R., Davis, L.: Gait recognition using image self-similarity. EURASIP Journal on Applied Signal Processing 2004(4), 572–585 (2004)
Zheng, S., Zhang, J., Huang, K., He, R., Tan, T.: Robust View Transformation Model for Gait Recognition. In: International Conference on Image Processing (ICIP), Brussels, Belgium (2011)
Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition Letters 30(11), 977–984 (2009)
Dadashi, F., Araabi, B.N., Soltanian-Zadeh, H.: Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines. In: 2nd International Congress on Image and Signal Processing, Tianjin, pp. 1–5 (October 2009)
Hu, M., Wang, Y., Zhang, Z., Wang, Y.: Combining spatial and temporal information for gait based gender classification. In: 20th International Conference on Pattern Recognition (ICPR), pp. 3679–3682 (August 2010)
Ephzibah, E.P.: Cost Effective Approach on Feature Selection using Genetic and Fuzzy Logic for Diabetes Diagnosis. International Journal on Soft Computing (IJSC) 2(1), 1–10 (2011)
Dash, M., Liu, H., Motoda, H.: Consistency Based Feature Selection. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 98–109. Springer, Heidelberg (2000)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. In: Motoda, H., Liu, H. (eds.) A Data Mining Perspective, ch. 8. Kluwer, Dordrecht (1998)
Chtioui, Y., Bertrand, D., Barba, D.: Feature selection by a genetic algorithm, application to seed discrimination by artificial vision. J. Sci. Food Agric. 76, 77–86 (1998)
Howe, N.R., Deschamps, A.: Better foreground segmentation through graph cuts. Computer Vision and Pattern Recognition 94(11), 49–52 (2007)
Wagg, D.K., Nixon, M.S.: Model-based gait enrolment in real-world imagery. In: Workshop on Multimodal User Authentication, Santa Barbara, CA, USA, pp. 11–12 (December 2003)
Ekinci, M., Aykut, M.: Human Gait Recognition based on Kernel PCA Using Projections. Journal of Computer Science and Technology 22(6), 867–876 (2007)
Chin, T.J., Suter, D.: Incremental Kernel Principal Component Analysis. IEEE Transactions on Image Processing 16(6), 1662–1674 (2007)
Yu, S., Tan, D., Tan, T.: A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition. In: Proceeding of the 18′th International Conference on Pattern Recognition (ICPR), Hong Kong, China (August 2006)
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Tafazzoli, F., Bebis, G., Louis, S., Hussain, M. (2014). Improving Human Gait Recognition Using Feature Selection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_80
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DOI: https://doi.org/10.1007/978-3-319-14364-4_80
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