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General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
October 2007 (vol. 29 no. 10)
pp. 1700-1715
The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not.We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

[1] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces versus Fisherfaces: Recognition Using Class-Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[2] C. BenAbdelkader and L.S. Davis, “Detection of People Carrying Objects: A Motion-Based Recognition Approach,” Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 378-383, 2002.
[3] A. Bobick and A. Johnson, “Gait Recognition Using Static Activity-Specific Parameters,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 423-430, 2001.
[4] J.E. Boyd, “Synchronization of Oscillations for Machine Perception of Gaits,” Computer Vision and Image Understanding, vol. 96, no. 1, pp. 35-59, 2004.
[5] L.F. Chen, H.Y. Liao, M.T. Ko, J.C. Lin, and G.J. Yu, “A New LDA-Based Face Recognition System which Can Solve the Small Sample Size Problem,” Pattern Recognition, vol. 33, no. 10, pp. 1713-1726, 2000.
[6] R.T. Collins, R. Bross, and J. Shi, “Silhouette-Based Human Identification from Body Shape and Gait,” Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 351-356, 2002.
[7] D. Cunado, M. Nixon, and J. Carter, “Automatic Extraction and Description of Human Gait Models for Recognition Purposes,” Computer Vision and Image Understanding, vol. 90, no. 1, pp. 1-41, 2003.
[8] R. Cutler and L. Davis, “Robust Periodic Motion and Motion Symmetry Detection,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 615-622, 2000.
[9] J. Cutting and D. Proffitt, “Gait Perception as an Example of How We May Perceive Events,” Intersensory Perception and Sensory Integration, vol. 2, pp. 249-273, 1981.
[10] J.G. Daugman, “Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile,” Vision Research, vol. 20, pp. 847-856, 1980.
[11] J.G. Daugman, “Uncertainty Relation for Resolution in Space, Spatial Frequency and Orientation Optimized by Two-Dimensional Visual Cortical Filters,” J. Optical Soc. Am., vol. 2, no. 7, pp.1160-1169, 1985.
[12] J.W. Davis and A.F. Bobick, “The Representation and Recognition of Human Movement Using Temporal Templates,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 928-934, 1997.
[13] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, second ed. Wiley-Interscience, 2000.
[14] D. Dunn, W.E. Higgins, and J. Wakeley, “Texture Segmentation Using 2D Gabor Elementary Functions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 130-149, Feb. 1994.
[15] K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. Academic Press Professional, 1990.
[16] J. Han and B. Bhanu, “Statistical Feature Fusion for Gait-Based Human Recognition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 842-847, 2004.
[17] I. Haritaoglu, R. Cutler, D. Harwood, and L. Davis, “Backpack: Detection of People Carrying Objects Using Silhouettes,” Computer Vision and Image Understanding, vol. 6, no. 3, pp. 385-397, 2001.
[18] G. Johansson, “Visual Motion Perception,” Scientific Am., vol. 232, pp. 76-88, 1975.
[19] A. Kale, A. Sundaresan, A.N. Rajagopalan, N.P. Cuntoor, A.K. Roy-Chowdhury, V. Kruger, and R. Chellappa, “Identification of Humans Using Gait,” IEEE Trans. Image Processing, vol. 13, no. 9, pp. 1163-1173, 2004.
[20] L.D. Lathauwer, “Signal Processing Based on Multilinear Algebra,” PhD dissertation, Katholike Universiteit Leuven, 1997.
[21] L. Lee, G. Dalley, and K. Tieu, “Learning Pedestrian Models for Silhouette Refinement,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 663-670, 2003.
[22] L. Lee and W.E.L. Grimson, “Gait Analysis for Recognition and Classification,” Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 155-162, 2002.
[23] T.S. Lee, “Image Representation Using 2D Gabor Wavelets,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp.959-971, Oct. 2003.
[24] J.J. Little and J.E. Boyd, “Recognizing People by Their Gait: The Shape of Motion,” Videre, vol. 1, no. 2, pp. 1-32, 1998.
[25] C. Liu, “Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 572-581, May 2004.
[26] C. Liu and H. Wechsler, “Enhanced Fisher Linear Discriminant Models for Face Recognition,” Proc. IEEE Int'l Conf. Pattern Recognition, vol. 2, pp. 1368-1372, 1998.
[27] C. Liu and H. Wechsler, “Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition,” IEEE Trans. Image Processing, vol. 11, no. 4, pp. 467-476, 2002.
[28] F. Liu and R.W. Picard, “Finding Periodicity in Space and Time,” Proc. IEEE Int'l Conf. Computer Vision, pp. 376-383, 1998.
[29] Y. Liu, R.T. Collins, and Y. Tsin, “Gait Sequence Analysis Using Frieze Patterns,” Proc. European Conf. Computer Vision, vol. 2, pp.657-671, 2002.
[30] Z. Liu and S. Sarkar, “Simplest Representation yet for Gait Recognition: Averaged Silhouette,” Proc. IEEE Int'l Conf. Pattern Recognition, vol. 4, pp. 211-214, 2004.
[31] S. Marcelja, “Mathematical Description of the Responses of Simple Cortical Cells,” J. Optical Soc. Am., vol. 70, no. 11, pp. 1297-1300, 1980.
[32] T. Moeslund and E. Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 231-268, 2001.
[33] M. Murray, A. Drought, and R. Kory, “Walking Pattern of Normal Men,” J. Bone and Joint Surgery, vol. 46-A, no. 2, pp. 335-360, 1964.
[34] S. Sarkar, P. Phillips, Z. Liu, I. Vega, P. Grother, and K. Bowyer, “The HumanID Gait Challenge Problem: Data Sets, Performance and Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 162-177, Feb. 2005.
[35] D.L. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligences, vol. 18, no. 8, pp. 831-836, Aug. 1996.
[36] R. Tanawongsuwan and A. Bobick, “Gait Recognition from Time-Normalized Joint-Angle Trajectories in the Walking Plane,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp.726-731, 2001.
[37] R. Tanawongsuwan and A. Bobick, “Modelling the Effects of Walking Speed on Appearance-Based Gait Recognition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp.783-790, 2004.
[38] D. Tao, X. Li, X. Wu, and S.J. Maybank, “Human Carrying Status in Visual Surveillance,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1670-1677, 2006.
[39] M.A.O. Vasilescu and D. Terzopoulos, “Multilinear Subspace Analysis for Image Ensembles,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 93-99, 2003.
[40] L. Wang, T. Tan, H. Ning, and W. Hu, “Silhouette Analysis-Based Gait Recognition for Human Identification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1505-1518, Dec. 2003.
[41] L. Wang, Y. Zhang, and J. Feng, “On the Euclidean Distance of Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1334-1339, Aug. 2005.
[42] X. Wang and X. Tang, “Dual-Space Linear Discriminant Analysis for Face Recognition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 564-569, 2004.
[43] J. Ye, R. Janardan, and Q. Li, “Two-Dimensional Linear Discriminant Analysis,” Neural Information Processing Systems, pp. 1569-1576, 2005.
[44] H. Yu and J. Yang, “A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition,” Pattern Recognition, vol. 34, no. 12, pp. 2067-2070, 2001.

Index Terms:
Gabor Gait, General Tensor Discriminant Analysis, Human Gait Recognition, Linear Discriminant Analysis, Tensor Rank, Visual Surveillance
Citation:
Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1700-1715, Oct. 2007, doi:10.1109/TPAMI.2007.1096
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