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Robust gait recognition: a comprehensive survey

Robust gait recognition: a comprehensive survey

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Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

References

    1. 1)
      • 1. Hayfron Acquah, J.B., Nixon, M.S., Carter, J.N.: ‘Automatic gait recognition by symmetry analysis’, Pattern Recognit. Lett., 2003, 24, (13), pp. 21752183.
    2. 2)
      • 2. Matovski, D.S., Nixon, M.S., Mahmoodi, S., et al: ‘The effect of time on gait recognition performance’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (2), pp. 543552.
    3. 3)
      • 3. Yu, S., Tan, D., Tan, T.: ‘A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition’. Int. Conf. Pattern Recognition, 2006, Hong Kong, China, 2006, vol. 4, pp. 441444.
    4. 4)
      • 4. Han, J., Bhanu, B.: ‘Individual recognition using gait energy image’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (2), pp. 316322.
    5. 5)
      • 5. Lee, T.K., Belkhatir, M., Sanei, S.: ‘A comprehensive review of past and present vision-based techniques for gait recognition’, Multimedia Tools Appl.., 2014, 72, (3), pp. 28332869.
    6. 6)
      • 6. Zhang, Z., Hu, M., Wang, Y.: ‘A survey of advances in biometric gait recognition’, Biometric Recognition, 2011, pp. 150158.
    7. 7)
      • 7. Chai, Y., Ren, J., Han, W., et al: ‘Human gait recognition: approaches, datasets and challenges’. 4th Int. Conf. Imaging for Crime Detection and Prevention 2011 (ICDP 2011), London, UK, 2011.
    8. 8)
      • 8. Liu, L.F., Jia, W., Zhu, Y.H.: ‘Survey of gait recognition’, Emerg. Intell. Compu. Technol. Appl. Asp. Artif. Intell., 2009, pp. 652659.
    9. 9)
      • 9. Bhowmik, S., Ghosh, A.K., Debsinha, J., et al: ‘A literature survey on human identification by gait’, Imperial J. Interdiscip Res., 2016, 2, (7).
    10. 10)
      • 10. Makihara, Y., Matovski, D.S., Nixon, M.S., et al: ‘Gait recognition: databases, representations, and applications’ (Wiley Encyclopedia of Electrical and Electronics Engineering, USA, 2015).
    11. 11)
      • 11. Connor, P., Ross, A.: ‘Biometric recognition by gait: A survey of modalities and features’, Comput. Vis. Image Underst., 2018, 167, pp. 127.
    12. 12)
      • 12. Schölkopf, B., Smola, A.J.: ‘Learning with kernels: support vector machines, regularization, optimization, and beyond’ (MIT press, USA, 2002).
    13. 13)
      • 13. Nixon, M., et al: ‘Model-based gait recognition’, 2009.
    14. 14)
      • 14. Wagg, D.K., Nixon, M.S.: ‘On automated model-based extraction and analysis of gait’. IEEE Int. Conf. Automatic Face and Gesture Recognition, 2004, 2004, pp. 1116.
    15. 15)
      • 15. Wang, L., Ning, H., Tan, T., et al: ‘Fusion of static and dynamic body biometrics for gait recognition’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (2), pp. 149158.
    16. 16)
      • 16. Bobick, A.E., Johnson, A.Y.: ‘Gait recognition using static, activity-specific parameters’, IEEE Comput. Vis. Pattern Recogn., 2001, 1, pp. I-423.
    17. 17)
      • 17. Tanawongsuwan, R., Bobick, A.: ‘Gait recognition from time-normalized jointangle trajectories in the walking plane’, IEEE Comput. Vis. Pattern Recogn., 2001, 2, p. II-726.
    18. 18)
      • 18. BenAbdelkader, C., Cutler, R., Davis, L.: ‘Stride and cadence as a biometric in automatic person identification and verification’. IEEE Int. Conf. Automatic Face and Gesture Recognition, Washington, DC, USA, May 2002, pp. 372377.
    19. 19)
      • 19. Boulgouris, N.V., Chi, Z.X.: ‘Human gait recognition based on matching of body components’, Pattern Recogn., 2007, 40, (6), pp. 17631770.
    20. 20)
      • 20. Cunado, D., Nixon, M.S., Carter, J.N.: ‘Automatic extraction and description of human gait models for recognition purposes’, Comput. Vis. Image Underst., 2003, 90, (1), pp. 141.
    21. 21)
      • 21. Zeng, W., Wang, C., Li, Y.: ‘Model-based human gait recognition via deterministic learning’, Cogn. Comput., 2014, 6, (2), pp. 218229.
    22. 22)
      • 22. Bouchrika, I., Carter, J.N., Nixon, M.S.: ‘Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras’, Mult. Tools Appl., 2016, 75, (2), pp. 12011221.
    23. 23)
      • 23. Yeoh, T.W., Daolio, F., Aguirre, H.E., et al: ‘On the effectiveness of feature selection methods for gait classification under different covariate factors’, Appl. Soft Comput., 2017, 61, pp. 4257.
    24. 24)
      • 24. Khamsemanan, N., Nattee, C., Jianwattanapaisarn, N.: ‘Human identification from freestyle walks using posture-based gait feature’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (1), pp. 119128.
    25. 25)
      • 25. Deng, M., Wang, C., Cheng, F., et al: ‘Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning’, Pattern Recogn., 2017, 67, pp. 186200.
    26. 26)
      • 26. Lee, L., Grimson, W.E.L.: ‘Gait analysis for recognition and classification’. IEEE Int. Conf. Automatic Face and Gesture Recognition, 2002, Washington, DC, USA, May 2002, pp. 148155.
    27. 27)
      • 27. Dockstader, S.L., Berg, M.J., Tekalp, A.M.: ‘Stochastic kinematic modeling and feature extraction for gait analysis’, IEEE Trans. Image Process., 2003, 12, (8), pp. 962976.
    28. 28)
      • 28. Zhang, J., Collins, R., Liu, Y.: ‘Representation and matching of articulated shapes’, IEEE Comput. Vis. Pattern Recogn., 2004, 2, p. II-342.
    29. 29)
      • 29. Zhang, R., Vogler, C., Metaxas, D.: ‘Human gait recognition at sagittal plane’, Image Vis. Comput., 2007, 25, (3), pp. 321330.
    30. 30)
      • 30. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: ‘A full-body layered deformable model for automatic model-based gait recognition’, EURASIP J.Adv. Signal Process., 2007, 2008, (1), pp. 113.
    31. 31)
      • 31. Ariyanto, G., Nixon, M.S.: ‘Marionette mass-spring model for 3d gait biometrics’. Int. Conf. Biometrics, 2012, New Delhi, India, 2012, pp. 354359.
    32. 32)
      • 32. Yoo, J.H., Hwang, D., Moon, K.Y., et al: ‘Automated human recognition by gait using neural network’. Workshops on Image Processing Theory, Tools and Applications, 2008, Sousse, Tunisia, November 2008, pp. 16.
    33. 33)
      • 33. Tafazzoli, F., Safabakhsh, R.: ‘Model-based human gait recognition using leg and arm movements’, Eng. Appl. Artif. Intell., 2010, 23, (8), pp. 12371246.
    34. 34)
      • 34. BenAbdelkader, C., Cutler, R., Nanda, H., et al: ‘Eigengait: motion-based recognition of people using image self-similarity’ in Bigun, J., Smeraldi, F. (Eds.): ‘Audio and video based biometric person authentication. AVBPA 2001.Lecture Notes in Computer Science, vol 2091, (Springer, Berlin, Heidelberg, 2001), pp. 284294.
    35. 35)
      • 35. Liu, Z., Sarkar, S.: ‘Simplest representation yet for gait recognition: averaged silhouette’. Int. Conf. Pattern Recognition, 2004, Cambridge, UK, 2004, vol. 4, pp. 211214.
    36. 36)
      • 36. Sivapalan, S., Chen, D., Denman, S., et al: ‘Gait energy volumes and frontal gait recognition using depth images’. In: Int. Joint Conf. Biometrics, 2011, Washington, DC, USA, 2011, pp. 16.
    37. 37)
      • 37. Ioannidis, D., Tzovaras, D., Damousis, I.G., et al: ‘Gait recognition using compact feature extraction transforms and depth information’, IEEE Trans. Inf. Forensics Sec., 2007, 2, (3), pp. 623630.
    38. 38)
      • 38. Afendi, T., Kurugollu, F., Crookes, D., et al: ‘A frontal view gait recognition based on 3d imaging using a time of flight camera’. 22nd European Signal Processing Conf., 2014, Lisbon, Portugal, 2014, pp. 24352439.
    39. 39)
      • 39. Zou, Q., Ni, L., Wang, Q., et al: ‘Robust gait recognition by integrating inertial and RGBD sensors’, IEEE Trans. Cybern., 2018, 48, (4), pp. 11361150.
    40. 40)
      • 40. Veres, G.V., Gordon, L., Carter, J.N., et al: ‘What image information is important in silhouette-based gait recognition?’, IEEE Comput. Vis. Pattern Recogn., 2004, 2, pp. IIII.
    41. 41)
      • 41. Kale, A., Sundaresan, A., Rajagopalan, A., et al: ‘Identification of humans using gait’, IEEE Trans. Image Process., 2004, 13, (9), pp. 11631173.
    42. 42)
      • 42. Liu, Z., Sarkar, S.: ‘Improved gait recognition by gait dynamics normalization’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (6), pp. 863876.
    43. 43)
      • 43. Kale, A., Rajagopalan, A., Cuntoor, N., et al: ‘Gait-based recognition of humans using continuous hmms’. In: IEEE Int. Conf. Automatic Face and Gesture Recognition, 2002, Washington, DC, USA, May 2002, pp. 336341.
    44. 44)
      • 44. Collins, R.T., Gross, R., Shi, J.: ‘Silhouette-based human identification from body shape and gait’. IEEE Int. Conf. Automatic Face and Gesture Recognition, 2002, Washington, DC, USA, May 2002, pp. 366371.
    45. 45)
      • 45. Wang, L., Tan, T., Hu, W., et al: ‘Automatic gait recognition based on statistical shape analysis’, IEEE Trans. Image Process., 2003, 12, (9), pp. 11201131.
    46. 46)
      • 46. Kent, J.T.: ‘New directions in shape analysis’, The Art Stat. Sci., 1992, pp. 115127.
    47. 47)
      • 47. Lee, S., Liu, Y., Collins, R.: ‘Shape variation-based frieze pattern for robust gait recognition’, IEEE Comput. Vis. Pattern Recogn., 2007, 2007, pp. 18.
    48. 48)
      • 48. Choudhury, S.D., Tjahjadi, T.: ‘Gait recognition based on shape and motion analysis of silhouette contours’, Comput. Vis. Image Underst., 2013, 117, (12), pp. 17701785.
    49. 49)
      • 49. Zeng, W., Wang, C., Yang, F.: ‘Silhouette-based gait recognition via deterministic learning’, Pattern Recogn., 2014, 47, (11), pp. 35683584.
    50. 50)
      • 50. Zhang, L., Zhang, L., Tao, D., et al: ‘A sparse and discriminative tensor to vector projection for human gait feature representation’, Signal Process., 2015, 106, pp. 245252.
    51. 51)
      • 51. Lam, T.H., Lee, R.S., Zhang, D.: ‘Human gait recognition by the fusion of motion and static spatio-temporal templates’, Pattern Recogn., 2007, 40, (9), pp. 25632573.
    52. 52)
      • 52. Choudhury, S.D., Tjahjadi, T.: ‘Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors’, Pattern Recogn., 2012, 45, (9), pp. 34143426.
    53. 53)
      • 53. Lee, C.P., Tan, A.W., Tan, S.C.: ‘Gait recognition via optimally interpolated deformable contours’, Pattern Recogn. Letters, 2013, 34, (6), pp. 663669.
    54. 54)
      • 54. Deng, M., Wang, C., Chen, Q.: ‘Human gait recognition based on deterministic learning through multiple views fusion’, Pattern Recogn. Lett., 2016, 78, pp. 5663.
    55. 55)
      • 55. Bengua, J.A., Ho, P.N., Tuan, H.D., et al: ‘Matrix product state for higher order tensor compression and classification’, IEEE Trans. Signal Process., 2016, 65, (15), pp. 40194030.
    56. 56)
      • 56. Tsuji, A., Makihara, Y., Yagi, Y.: ‘Silhouette transformation based on walking speed for gait identification’. IEEE Conf. Computer Vision and Pattern Recognition, 2010, 2010, pp. 717722.
    57. 57)
      • 57. El Alfy, H., Mitsugami, I., Yagi, Y.: ‘Gait recognition based on normal distance maps’, IEEE Trans. Cybern., 2018, 48, (5), pp. 15261539.
    58. 58)
      • 58. Wang, L., Tan, T., Ning, H., et al: ‘Silhouette analysis-based gait recognition for human identification’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (12), pp. 15051518.
    59. 59)
      • 59. BenAbdelkader, C., Cutler, R.G., Davis, L.S.: ‘Gait recognition using image self-similarity’, EURASIP J. Adv. Signal Process., 2004, 2004, (4), pp. 114.
    60. 60)
      • 60. Kobayashi, T., Otsu, N.: ‘Action and simultaneous multiple-person identification using cubic higher-order local auto-correlation’. Int. Conf. Pattern Recognition, Cambridge, UK, August 2004, vol. 4, pp. 741744.
    61. 61)
      • 61. Otsu, N., Kurita, T.: ‘A new scheme for practical flexible and intelligent vision systems’ (MVA, Tokyo, Japan, 1988), pp. 431435.
    62. 62)
      • 62. Lu, J., Zhang, E.: ‘Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion’, Pattern Recogn. Letters, 2007, 28, (16), pp. 24012411.
    63. 63)
      • 63. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: ‘MPCA: multilinear principal component analysis of tensor objects’, IEEE Trans. Neural Netw., 2008, 19, (1), pp. 1839.
    64. 64)
      • 64. Xu, D., Yan, S., Tao, D., et al: ‘Human gait recognition with matrix representation’, IEEE Trans. Circuits Syst. Video Technol., 2006, 16, (7), pp. 896903.
    65. 65)
      • 65. Tao, D., Li, X., Wu, X., et al: ‘General tensor discriminant analysis and Gabor features for gait recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (10), pp. 17001715.
    66. 66)
      • 66. Xu, D., Yan, S., Tao, D., et al: ‘Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval’, IEEE Trans. Image Process., 2007, 16, (11), pp. 28112821.
    67. 67)
      • 67. Li, X., Lin, S., Yan, S., et al: ‘Discriminant locally linear embedding with high-order tensor data’, IEEE Trans. Syst., Man, Cybern., Part B (Cybern.), 2008, 38, (2), pp. 342352.
    68. 68)
      • 68. Chen, C., Zhang, J., Fleischer, R.: ‘Distance approximating dimension reduction of Riemannian manifolds’, IEEE Trans. Sys., Man, Cybern., Part B (Cybern.), 2010, 40, (1), pp. 208217.
    69. 69)
      • 69. Huang, Y., Xu, D., Cham, T.J.: ‘Face and human gait recognition using imageto-class distance’, IEEE Trans. Circuits Syst. Video Technol., 2010, 20, (3), pp. 431438.
    70. 70)
      • 70. Zhang, J., Pu, J., Chen, C., et al: ‘Low-resolution gait recognition’, IEEE Trans. Syst. Man Cybern., Part B (Cybern.), 2010, 40, (4), pp. 986996.
    71. 71)
      • 71. Xu, D., Huang, Y., Zeng, Z., et al: ‘Human gait recognition using patch distribution feature and locality-constrained group sparse representation’, IEEE Trans. Image Process., 2012, 21, (1), pp. 316326.
    72. 72)
      • 72. Lai, Z., Xu, Y., Jin, Z., et al: ‘Human gait recognition via sparse discriminant projection learning’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (10), pp. 16511662.
    73. 73)
      • 73. Lu, J., Wang, G., Moulin, P.: ‘Human identity and gender recognition from gait sequences with arbitrary walking directions’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (1), pp. 5161.
    74. 74)
      • 74. Martín Félez, R., Xiang, T.: ‘Uncooperative gait recognition by learning to rank’, Pattern Recogn., 2014, 47, (12), pp. 37933806.
    75. 75)
      • 75. Lishani, A.O., Boubchir, L., Bouridane, A.: ‘Haralick features for GEI-based human gait recognition’. Int. Conf. Microelectronics, 2014, Doha, Qatar, December 2014, pp. 3639.
    76. 76)
      • 76. Guan, Y., Li, C.T., Roli, F.: ‘On reducing the effect of covariate factors in gait recognition: a classifier ensemble method’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (7), pp. 15211528.
    77. 77)
      • 77. Xing, X., Wang, K., Yan, T., et al: ‘Complete canonical correlation analysis with application to multi-view gait recognition’, Pattern Recogn., 2016, 50, pp. 107117.
    78. 78)
      • 78. Rida, I., Boubchir, L., Al Maadeed, N., et al: ‘Robust model-free gait recognition by statistical dependency feature selection and globality-locality preserving projections’. Int. Conf. Telecommunications and Signal Processing, 2016, Vienna, Austria, June 2016, pp. 652655.
    79. 79)
      • 79. Lishani, A.O., Boubchir, L., Khalifa, E., et al: ‘Gabor filter bank-based GEI features for human gait recognition’. 39th Int. Conf. Telecommunications and Signal Processing, 2016, Vienna, Austria, June 2016, pp. 648651.
    80. 80)
      • 80. Wang, X., Wang, J., Yan, K.: ‘Gait recognition based on Gabor wavelets and (2d) 2pca’, Multimedia Tools Appl.., 2017, pp. 117.
    81. 81)
      • 81. Ma, G., Wu, L., Wang, Y.: ‘A general subspace ensemble learning framework via totally-corrective boosting and tensor-based and local patch-based extensions for gait recognition’, Pattern Recogn., 2017, 66, pp. 280294.
    82. 82)
      • 82. Ma, G., Wang, Y., Wu, L.: ‘Subspace ensemble learning via totally-corrective boosting for gait recognition’, Neurocomputing, 2017, 224, pp. 119127.
    83. 83)
      • 83. Ben, X., Zhang, P., Meng, W., et al: ‘On the distance metric learning between cross-domain gaits’, Neurocomputing, 2016, 208, pp. 153164.
    84. 84)
      • 84. Chen, X., Xu, J.: ‘Uncooperative gait recognition: Re-ranking based on sparse coding and multi-view hypergraph learning’, Pattern Recogn., 2016, 53, pp. 116129.
    85. 85)
      • 85. Lee, H., Baek, J., Kim, E.: ‘A probabilistic image-weighting scheme for robust silhouette-based gait recognition’, Multimedia Tools Appl.., 2014, 70, (3), pp. 13991419.
    86. 86)
      • 86. Chhatrala, R., Patil, S., Lahudkar, S., et al: ‘Sparse multilinear Laplacian discriminant analysis for gait recognition’, Pattern Anal. Appl., 2017, pp. 114.
    87. 87)
      • 87. Hossain, M.A., Makihara, Y., Wang, J., et al: ‘Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control’, Pattern Recogn., 2010, 43, (6), pp. 22812291.
    88. 88)
      • 88. Li, N., Xu, Y., Yang, X.K.: ‘Part-based human gait identification under clothing and carrying condition variations’. Int. Conf. Machine Learning and Cybernetics, 2010, Qingdao, China, 2010, vol. 1, pp. 268273.
    89. 89)
      • 89. Choudhury, S.D., Tjahjadi, T.: ‘Robust view-invariant multiscale gait recognition’, Pattern Recogn., 2015, 48, (3), pp. 798811.
    90. 90)
      • 90. Verlekar, T.T., Correia, P.L., Soares, L.D.: ‘View-invariant gait recognition system using a gait energy image decomposition method’, IET Biometrics, 2017, 6, (4), pp. 299306.
    91. 91)
      • 91. Aggarwal, H., Vishwakarma, D.: ‘Covariate conscious approach for gait recognition based upon Zernike moment invariants’, IEEE Trans. Cogn. Dev. Syst., 2017, 10, (2), pp. 397407.
    92. 92)
      • 92. Li, X., Chen, Y.: ‘Gait recognition based on structural gait energy image’ J.Comput. Inf. Syst., 2013, 9, (1), pp. 121126.
    93. 93)
      • 93. Iwashita, Y., Uchino, K., Kurazume, R.: ‘Gait-based person identification robust to changes in appearance’, Sensors, 2013, 13, (6), pp. 78847901.
    94. 94)
      • 94. Gabriel Sanz, S., Vera Rodriguez, R., Tome, P., et al: ‘Assessment of gait recognition based on the lower part of the human body’. Int. Workshop on Biometrics and Forensics, 2013, Lisbon, Portugal, 2013, pp. 14.
    95. 95)
      • 95. Islam, M.S., Islam, M.R., Akter, M.S., et al: ‘Window based clothing invariant gait recognition’. Int. Conf. Advances in Electrical Engineering, 2013, Dhaka, Bangladesh, 2013, pp. 411414.
    96. 96)
      • 96. Nandy, A., Chakraborty, R., Chakraborty, P.: ‘Cloth invariant gait recognition using pooled segmented statistical features’, Neurocomputing, 2016, 191, pp. 117140.
    97. 97)
      • 97. Lishani, A.O., Boubchir, L., Khalifa, E., et al: ‘Human gait recognition based on Haralick features’, Signal, Image Video Process., 2017, 11, pp. 18.
    98. 98)
      • 98. Bashir, K., Xiang, T., Gong, S.: ‘Feature selection on gait energy image for human identification’. IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2008, Las Vegas, NV, USA, 2008, pp. 985988.
    99. 99)
      • 99. Dupuis, Y., Savatier, X., Vasseur, P.: ‘Feature subset selection applied to model-free gait recognition’, Image Vis. Comput., 2013, 31, (8), pp. 580591.
    100. 100)
      • 100. Rida, I., Almaadeed, S., Bouridane, A.: ‘Improved gait recognition based on gait energy images’. Int. Conf. Microelectronics, 2014, Doha, Qatar, 2014, pp. 4043.
    101. 101)
      • 101. Rida, I., Bouridane, A., Marcialis, G.L., et al: ‘Improved human gait recognition’ in Murino, V., Puppo, E. (Eds.): ‘Image analysis and processing-ICIAP 2015.Lecture Notes in Computer Science, vol 9280 (Springer, Cham, 2015), pp. 119129.
    102. 102)
      • 102. Rokanujjaman, M., Islam, M.S., Hossain, M.A., et al: ‘Effective part-based gait identification using frequency-domain gait entropy features’, Multimedia Tools Appl.., 2015, 74, (9), pp. 30993120.
    103. 103)
      • 103. Whytock, T., Belyaev, A., Robertson, N.M.: ‘On covariate factor detection and removal for robust gait recognition’, Mach. Vis. Appl., 2015, 26, (5), pp. 661674.
    104. 104)
      • 104. Rida, I., Almaadeed, S., Bouridane, A.: ‘Gait recognition based on modified phase-only correlation’, Signal, Image Video Process., 2016, 10, (3), pp. 463470.
    105. 105)
      • 105. Rida, I., Jiang, X., Marcialis, G.L.: ‘Human body part selection by group lasso of motion for model-free gait recognition’, IEEE Signal Process. Lett., 2016, 23, (1), pp. 154158.
    106. 106)
      • 106. Rida, I., Al. Maadeed, N., Marcialis, G.L., et al: ‘Improved model-free gait recognition based on human body part’ in Jiang, R., Al-maadeed, S., Bouridane, A., et al (Eds.): ‘Biometric Security and Privacy. Signal Processing for Security Technologies’ (Springer, Cham, 2017), pp. 141161.
    107. 107)
      • 107. Alotaibi, M., Mahmood, A.: ‘Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding’, Signal, Image Video Process., 2017, 11, pp. 18.
    108. 108)
      • 108. Alotaibi, M., Mahmood, A.: ‘Reduction of gait covariate factors using feature selection and sparse dictionary learning’. IEEE Int. Symp. Multimedia, 2016, San Jose, CA, USA, 2016, pp. 337340.
    109. 109)
      • 109. Isaac, E., Elias, S., Rajagopalan, S., et al: ‘View-invariant gait recognition through genetic template segmentation’, arXiv preprint arXiv:170505273, 2017.
    110. 110)
      • 110. Ghebleh, A., Moghaddam, M.E.: ‘Clothing-invariant human gait recognition using an adaptive outlier detection method’, Multimed. Tools Appl., 2017, 77, pp. 121.
    111. 111)
      • 111. Liang, Y., Li, C.T., Guan, Y., et al: ‘Gait recognition based on the golden ratio’, EURASIP J. Image Video Process., 2016, 2016, (1), p. 22.
    112. 112)
      • 112. Dempster, W.T., Gaughran, G.R.: ‘Properties of body segments based on size and weight’, Dev. Dyn., 1967, 120, (1), pp. 3354.
    113. 113)
      • 113. Rida, I., Al Maadeed, S., Bouridane, A.: ‘Unsupervised feature selection method for improved human gait recognition’. 23rd European Signal Processing Conf., 2015, Nice, France, 2015, pp. 11281132.
    114. 114)
      • 114. Liu, J., Zheng, N.: ‘Gait history image: a novel temporal template for gait recognition’. IEEE Int. Conf. Multimedia and Expo, 2007, 2007, pp. 663666.
    115. 115)
      • 115. Ma, Q., Wang, S., Nie, D., et al: ‘Recognizing humans based on gait moment image’. Int. Conf. Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007, Qingdao, China, 2007, vol. 2, pp. 606610.
    116. 116)
      • 116. Yang, X., Zhou, Y., Zhang, T., et al: ‘Gait recognition based on dynamic region analysis’, Signal Process., 2008, 88, (9), pp. 23502356.
    117. 117)
      • 117. Chen, C., Liang, J., Zhao, H., et al: ‘Frame difference energy image for gait recognition with incomplete silhouettes’, Pattern Recogn. Lett., 2009, 30, (11), pp. 977984.
    118. 118)
      • 118. Bashir, K., Xiang, T., Gong, S., et al: ‘Gait representation using flow fields’ (BMVC, London, UK, 2009), pp. 111.
    119. 119)
      • 119. Shanableh, T., Assaleh, K., Al Hajjaj, L., et al: ‘Gait recognition system tailored for Arab costume of the gulf region’. IEEE Int. Symp. Signal Processing and Information Technology, 2009, Ajman, UAE, December 2009, pp. 544549.
    120. 120)
      • 120. Bashir, K., Xiang, T., Gong, S.: ‘Gait recognition without subject cooperation’, Pattern Recogn. Lett., 2010, 31, (13), pp. 20522060.
    121. 121)
      • 121. Wang, C., Zhang, J., Pu, J., et al: ‘Chrono-gait image: a novel temporal template for gait recognition’. European Conf. Computer Vision, Crete, Greece, 2010, vol. 2010, pp. 257270.
    122. 122)
      • 122. Wang, C., Zhang, J., Wang, L., et al: ‘Human identification using temporal information preserving gait template’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (11), pp. 21642176.
    123. 123)
      • 123. Zhang, E., Zhao, Y., Xiong, W.: ‘Active energy image plus 2dlpp for gait recognition’, Signal Process.., 2010, 90, (7), pp. 22952302.
    124. 124)
      • 124. Lam, T.H., Cheung, K.H., Liu, J.N.: ‘Gait flow image: a silhouette-based gait representation for human identification’, Pattern Recogn., 2011, 44, (4), pp. 973987.
    125. 125)
      • 125. Roy, A., Sural, S., Mukherjee, J.: ‘Gait recognition using pose kinematics and pose energy image’, Signal Process., 2012, 92, (3), pp. 780792.
    126. 126)
      • 126. Hofmann, M., Rigoll, G.: ‘Improved gait recognition using gradient histogram energy image’. IEEE Int. Conf. Image Processing, 2012, 2012, pp. 13891392.
    127. 127)
      • 127. Huang, X., Boulgouris, N.V.: ‘Gait recognition with shifted energy image and structural feature extraction’, IEEE Trans. Image Process., 2012, 21, (4), pp. 22562268.
    128. 128)
      • 128. Jeevan, M., Jain, N., Hanmandlu, M., et al: ‘Gait recognition based on gait pal and pal entropy image’. IEEE Int. Conf. Image Processing, 2013, Melbourne, VIC, Australia, September 2013, pp. 41954199.
    129. 129)
      • 129. Boulgouris, N.V., Chi, Z.X.: ‘Gait recognition using radon transform and linear discriminant analysis’, IEEE Trans. Image Process., 2007, 16, (3), pp. 731740.
    130. 130)
      • 130. Lee, C.P., Tan, A.W., Tan, S.C.: ‘Gait probability image: an information-theoretic model of gait representation’, J. Vis. Commun. Image Represent., 2014, 25, (6), pp. 14891492.
    131. 131)
      • 131. Kusakunniran, W.: ‘Recognizing gaits on spatio-temporal feature domain’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (9), pp. 14161423.
    132. 132)
      • 132. Kusakunniran, W.: ‘Attribute-based learning for gait recognition using spatiotemporal interest points’, Image Vis. Comput., 2014, 32, (12), pp. 11171126.
    133. 133)
      • 133. Arora, P., Srivastava, S.: ‘Gait recognition using gait Gaussian image’. Int. Conf. Signal Processing and Integrated Networks, 2015, Noida, India, February 2015, pp. 791794.
    134. 134)
      • 134. Luo, J., Zhang, J., Zi, C., et al: ‘Gait recognition using GEI and AFDEI’, Int. J. Optics, 2015, 2015.
    135. 135)
      • 135. Al Tayyan, A., Assaleh, K., Shanableh, T.: ‘Decision-level fusion for single-view gait recognition with various carrying and clothing conditions’, Image Vis. Comput., 2017, 61, pp. 5469.
    136. 136)
      • 136. Lee, C.P., Tan, A.W., Tan, S.C.: ‘Gait recognition with transient binary patterns’, J. Vis. Commun. Image Represent., 2015, 33, pp. 6977.
    137. 137)
      • 137. Lee, C.P., Tan, A.W., Tan, S.C.: ‘Time-sliced averaged motion history image for gait recognition’, J. Vis. Commun. Image Represent., 2014, 25, (5), pp. 822826.
    138. 138)
      • 138. Arora, P., Hanmandlu, M., Srivastava, S.: ‘Gait based authentication using gait information image features’, Pattern Recogn. Lett., 2015, 68, pp. 336342.
    139. 139)
      • 139. Choudhury, S.D., Tjahjadi, T.: ‘Clothing and carrying condition invariant gait recognition based on rotation forest’, Pattern Recogn. Lett., 2016, 80, pp. 17.
    140. 140)
      • 140. Atta, R., Shaheen, S., Ghanbari, M.: ‘Human identification based on temporal lifting using 5/3 wavelet filters and radon transform’, Pattern Recogn., 2017, 69, pp. 213224.
    141. 141)
      • 141. Mu, Y., Tao, D.: ‘Biologically inspired feature manifold for gait recognition’, Neurocomputing, 2010, 73, (4), pp. 895902.
    142. 142)
      • 142. Hu, R., Shen, W., Wang, H.: ‘Recursive spatiotemporal subspace learning for gait recognition’, Neurocomputing, 2010, 73, (10), pp. 18921899.
    143. 143)
      • 143. Chaurasia, P., Yogarajah, P., Condell, J., et al: ‘Fusion of random walk and discrete Fourier spectrum methods for gait recognition’, IEEE Trans. Human-Machine Syst., 2017, 47, (6), pp. 751762.
    144. 144)
      • 144. Chhatrala, R., Jadhav, D.V.: ‘Multilinear Laplacian discriminant analysis for gait recognition’, IET Comput. Vis., 2016, 11, (2), pp. 153160.
    145. 145)
      • 145. Chen, J., Liu, J.: ‘Average gait differential image based human recognition’, The Sci. World J., 2014, 2014.
    146. 146)
      • 146. Verlekar, T.T., Correia, P.L., Soares, L.D.: ‘Sparse error gait image: a new representation for gait recognition’. Int. Workshop on Biometrics and Forensics, 2017, Coventry, UK, April 2017, pp. 16.
    147. 147)
      • 147. Medikonda, J., Madasu, H., Panigrahi, B.K.: ‘Information set based gait authentication system’, Neurocomputing, 2016, 207, pp. 114.
    148. 148)
      • 148. Hu, M., Wang, Y., Zhang, Z., et al: ‘Incremental learning for video-based gait recognition with LBP flow’, IEEE Trans. Cybern., 2013, 43, (1), pp. 7789.
    149. 149)
      • 149. Liu, Y., Zhang, J., Wang, C., et al: ‘Multiple hog templates for gait recognition’. In: Int. Conf. Pattern Recognition, 2012, Tsukuba, Japan, November 2012, pp. 29302933.
    150. 150)
      • 150. Arora, P., Srivastava, S., Arora, K., et al: ‘Improved gait recognition using gradient histogram Gaussian image’, Procedia Comput. Sci., 2015, 58, pp. 408413.
    151. 151)
      • 151. Hofmann, M., Schmidt, S.M., Rajagopalan, A.N., et al: ‘Combined face and gait recognition using alpha matte preprocessing’. Int. Conf. on Biometrics, New Delhi, India, 2012, pp. 390395.
    152. 152)
      • 152. Shakhnarovich, G., Lee, L., Darrell, T.: ‘Integrated face and gait recognition from multiple views’. IEEE Conf. Comput. Vis. Pattern Recogn., 2001, Kauai, HI, USA, December 2001, vol. 1, pp. II.
    153. 153)
      • 153. Almohammad, M.S., Salama, G.I., Mahmoud, T.A.: ‘Human identification system based on feature level fusion using face and gait biometrics’. Int. Conf. Engineering and Technology, 2012, Cairo, Egypt, 2012, pp. 15.
    154. 154)
      • 154. Makihara, Y., Sagawa, R., Mukaigawa, Y., et al: ‘Gait recognition using a view transformation model in the frequency domain’. Computer Vision–ECCV 2006, Graz, Austria, 2006, pp. 151163.
    155. 155)
      • 155. Kusakunniran, W., Wu, Q., Li, H., et al: ‘Multiple views gait recognition using view transformation model based on optimized gait energy image’. IEEE Int. Conf. Computer Vision Workshops, 2009, Kyoto, Japan, October 2009, pp. 10581064.
    156. 156)
      • 156. Zheng, S., Zhang, J., Huang, K., et al: ‘Robust view transformation model for gait recognition’. IEEE Int. Conf. Image Processing, 2011, Brussels, Belgium, September 2011, pp. 20732076.
    157. 157)
      • 157. Muramatsu, D., Shiraishi, A., Makihara, Y., et al: ‘Arbitrary view transformation model for gait person authentication’. IEEE Int. Conf. Biometrics: Theory, Applications and Systems, 2012, Arlington, VA, USA, September 2012, pp. 8590.
    158. 158)
      • 158. Muramatsu, D., Makihara, Y., Yagi, Y.: ‘View transformation model incorporating quality measures for cross-view gait recognition’, IEEE Trans. Cybern., 2016, 46, (7), pp. 16021615.
    159. 159)
      • 159. Kusakunniran, W., Wu, Q., Zhang, J., et al: ‘Support vector regression for multiview gait recognition based on local motion feature selection’. IEEE Conf. Computer Vision and Pattern Recognition, 2010, 2010, pp. 974981.
    160. 160)
      • 160. Kusakunniran, W., Wu, Q., Zhang, J., et al: ‘Gait recognition under various viewing angles based on correlated motion regression’, IEEE Trans. Circuits Syst. Video Technol., 2012, 22, (6), pp. 966980.
    161. 161)
      • 161. Kusakunniran, W., Wu, Q., Zhang, J., et al: ‘Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron’, Pattern Recogn. Lett., 2012, 33, (7), pp. 882889.
    162. 162)
      • 162. Muramatsu, D., Shiraishi, A., Makihara, Y., et al: ‘Gait-based person recognition using arbitrary view transformation model’, IEEE Trans. Image Process., 2015, 24, (1), pp. 140154.
    163. 163)
      • 163. Muramatsu, D., Makihara, Y., Yagi, Y.: ‘Cross-view gait recognition by fusion of multiple transformation consistency measures’, IET Biometrics, 2015, 4, (2), pp. 6273.
    164. 164)
      • 164. Huang, X., Boulgouris, N.V.: ‘Human gait recognition based on multiview gait sequences’, EURASIP J. Adv. Signal Process., 2008, 2008, (1), p. 629102.
    165. 165)
      • 165. Bashir, K., Xiang, T., Gong, S.: ‘Cross view gait recognition using correlation strength’ (BMVC, Aberystwyth, UK, 2010), pp. 111.
    166. 166)
      • 166. Liu, N., Lu, J., Tan, Y.P.: ‘Joint subspace learning for view-invariant gait recognition’, IEEE Signal Process. Lett., 2011, 18, (7), pp. 431434.
    167. 167)
      • 167. Xu, W., Luo, C., Ji, A., et al: ‘Coupled locality preserving projections for cross-view gait recognition’, Neurocomputing, 2017, 224, pp. 3744.
    168. 168)
      • 168. Hu, H.: ‘Enhanced Gabor feature based classification using a regularized locally tensor discriminant model for multiview gait recognition’, IEEE Trans. Circuits Syst. Video Technol., 2013, 23, (7), pp. 12741286.
    169. 169)
      • 169. Lu, J., Tan, Y.P.: ‘Uncorrelated discriminant simplex analysis for view-invariant gait signal computing’, Patt. Recogn. Lett., 2010, 31, (5), pp. 382393.
    170. 170)
      • 170. Hu, H.: ‘Multiview gait recognition based on patch distribution features and uncorrelated multilinear sparse local discriminant canonical correlation analysis’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (4), pp. 617630.
    171. 171)
      • 171. Mansur, A., Makihara, Y., Muramatsu, D., et al: ‘Cross-view gait recognition using view-dependent discriminative analysis’. IEEE Int. Joint Conf. Biometrics, 2014, Clearwater, FL, USA, October 2014, pp. 18.
    172. 172)
      • 172. Makihara, Y., Mansur, A., Muramatsu, D., et al: ‘Multi-view discriminant analysis with tensor representation and its application to cross-view gait recognition’. IEEE Int. Conf. Workshops on Automatic Face and Gesture Recognition, Ljubljana, Slovenia, May 2015, vol. 1, pp. 18.
    173. 173)
      • 173. Connie, T., Goh, K.O.M., Teoh, A.B.J.: ‘Multi-view gait recognition using a doubly-kernel approach on the Grassmann manifold’, Neurocomputing, 2016, 216, pp. 534542.
    174. 174)
      • 174. Connie, T., Goh, M.K.O., Teoh, A.B.J.: ‘A Grassmannian approach to address view change problem in gait recognition’, IEEE Trans. Cybern., 2017, 47, (6), pp. 13951408.
    175. 175)
      • 175. Liu, N., Lu, J., Yang, G., et al: ‘Robust gait recognition via discriminative set matching’, J. Vis. Commun. Image Represent., 2013, 24, (4), pp. 439447.
    176. 176)
      • 176. Hur, D., Wallraven, C., Lee, S.W.: ‘View invariant body pose estimation based on biased manifold learning’. 20th Int. Conf. Pattern Recogn., 2010, Istanbul, Turkey, August 2010, pp. 38663869.
    177. 177)
      • 177. Jia, N., Sanchez, V., Li, C.T.: ‘On view-invariant gait recognition: a feature selection solution’, IET Biometrics, 2018, 7, (4), pp. 287295.
    178. 178)
      • 178. Jean, F., Bergevin, R., Albu, A.B.: ‘Computing and evaluating view-normalized body part trajectories’, Image Vis. Comput., 2009, 27, (9), pp. 12721284.
    179. 179)
      • 179. Jean, F., Albu, A.B., Bergevin, R.: ‘Towards view-invariant gait modeling: computing view-normalized body part trajectories’, Pattern Recogn., 2009, 42, (11), pp. 29362949.
    180. 180)
      • 180. Goffredo, M., Bouchrika, I., Carter, J.N., et al: ‘Self-calibrating view invariant gait biometrics’, IEEE Trans. Syst. Man, Cybern., Part B (Cybern.), 2010, 40, (4), pp. 9971008.
    181. 181)
      • 181. Kusakunniran, W., Wu, Q., Zhang, J., et al: ‘A new view-invariant feature for cross-view gait recognition’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (10), pp. 16421653.
    182. 182)
      • 182. Zeng, W., Wang, C.: ‘View-invariant gait recognition via deterministic learning’, Neurocomputing, 2016, 175, pp. 324335.
    183. 183)
      • 183. Castro, F.M., Marín Jimenez, M.J., Medina Carnicer, R.: ‘Pyramidal fisher motion for multiview gait recognition’. IEEE Int. Conf. Pattern Recognition (ICPR), 2014, Stockholm, Sweden, August 2014, pp. 16921697.
    184. 184)
      • 184. Castro, F.M., Marín Jiménez, M.J., Mata, N.G., et al: ‘Fisher motion descriptor for multiview gait recognition’, Int. J. Pattern Recogn. Artif. Intell., 2017, 31, (01), p. 1756002.
    185. 185)
      • 185. Zhao, X., Jiang, Y., Stathaki, T., et al: ‘Gait recognition method for arbitrary straight walking paths using appearance conversion machine’, Neurocomputing, 2016, 173, pp. 530540.
    186. 186)
      • 186. Bodor, R., Drenner, A., Fehr, D., et al: ‘View-independent human motion classification using image-based reconstruction’, Image Vis. Comput., 2009, 27, (8), pp. 11941206.
    187. 187)
      • 187. Zhao, G., Liu, G., Li, H., et al: ‘3D gait recognition using multiple cameras’. In: IEEE Int. Conf. Automatic Face and Gesture Recognition, 2006, Southampton, UK, April 2006, pp. 529534.
    188. 188)
      • 188. Zhang, Z., Troje, N.F.: ‘View-independent person identification from human gait’, Neurocomputing, 2005, 69, (1), pp. 250256.
    189. 189)
      • 189. Tang, J., Luo, J., Tjahjadi, T., et al: ‘Robust arbitrary-view gait recognition based on 3D partial similarity matching’, IEEE Trans. Image Process., 2017, 26, (1), pp. 722.
    190. 190)
      • 190. Luo, J., Tang, J., Tjahjadi, T., et al: ‘Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis’, Pattern Recogn., 2016, B60, pp. 361377.
    191. 191)
      • 191. López Fernández, D., Madrid Cuevas, F.J., Carmona Poyato, A., et al: ‘Entropy volumes for viewpoint-independent gait recognition’, Mach. Vis. Appl., 2015, 26, (7–8), pp. 10791094.
    192. 192)
      • 192. López Fernández, D., Madrid Cuevas, F.J., Carmona Poyato, A., et al: ‘A new approach for multi-view gait recognition on unconstrained paths’, J. Vis. Commun. Image Represent., 2016, 38, pp. 396406.
    193. 193)
      • 193. Roy, A., Sural, S., Mukherjee, J., et al: ‘Occlusion detection and gait silhouette reconstruction from degraded scenes’, Signal Image Video Process., 2011, 5, (4), p. 415.
    194. 194)
      • 194. Roy, A., Chattopadhyay, P., Sural, S., et al: ‘Modelling, synthesis and characterisation of occlusion in videos’, IET Comput. Vis., 2015, 9, (6), pp. 821830.
    195. 195)
      • 195. Ortells, J., Mollineda, R.A., Mederos, B., et al: ‘Gait recognition from corrupted silhouettes: a robust statistical approach’, Mach. Vis. Appl., 2017, 28, (1–2), pp. 1533.
    196. 196)
      • 196. Wu, Z., Huang, Y., Wang, L., et al: ‘A comprehensive study on cross-view gait based human identification with deep CNNS’, IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39, (2), pp. 209226.
    197. 197)
      • 197. Wolf, T., Babaee, M., Rigoll, G.: ‘Multi-view gait recognition using 3D convolutional neural networks’. IEEE Int. Conf. Image Processing, 2016, Phoenix, AZ, USA, 2016, pp. 41654169.
    198. 198)
      • 198. Takemura, N., Makihara, Y., Muramatsu, D., et al: ‘On input/output architectures for convolutional neural network-based cross-view gait recognition’, IEEE Trans. Circuits Syst. Video Technol., 2017, https://doi.org/10.1109/TCSVT.2017.2760835.
    199. 199)
      • 199. Uddin, M.Z., Kim, M.R.: ‘A deep learning-based gait posture recognition from depth information for smart home applications’. Int. Conf. Computer Science and its Applications, Singapore, 2016, pp. 407413.
    200. 200)
      • 200. Feng, Y., Li, Y., Luo, J.: ‘Learning effective gait features using LSTM’. In: Int. Conf. Pattern Recognition, 2016, Cancun, Mexico, 2016, pp. 325330.
    201. 201)
      • 201. Yu, S., Chen, H., Wang, Q., et al: ‘Invariant feature extraction for gait recognition using only one uniform model’, Neurocomputing, 2017, 239, pp. 8193.
    202. 202)
      • 202. Yu, S., Chen, H., Reyes, E.B.G., et al: ‘Gaitgan: invariant gait feature extraction using generative adversarial networks’. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2017, Honolulu, Hawaii, 2017, pp. 532539.
    203. 203)
      • 203. Sarkar, S., Phillips, P.J., Liu, Z., et al: ‘The humanid gait challenge problem: data sets, performance, and analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (2), pp. 162177.
    204. 204)
      • 204. Guha, T., Ward, R.: ‘Differential radon transform for gait recognition’. IEEE Int. Conf. Acoustics Speech and Signal Processing, 2010, Dallas, TX, USA, 2010, pp. 834837.
    205. 205)
      • 205. Makihara, Y., Mannami, H., Tsuji, A., et al: ‘The OU-ISIR gait database comprising the treadmill dataset’, IPSJ Trans. Comput. Vis.Appl., 2012, 4, pp. 5362.
    206. 206)
      • 206. Gross, R., Shi, J.: ‘The CMU motion of body (MOBO) database’, Tech. Report, CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University, 2001.
    207. 207)
      • 207. Veeraraghavan, A., Chowdhury, A.R., Chellappa, R.: ‘Role of shape and kinematics in human movement analysis’. IEEE Conf. Computer Vision and Pattern Recognition, 2004, Washington, DC, USA, 2004, vol. 1, pp. II.
    208. 208)
      • 208. Veeraraghavan, A., Roy Chowdhury, A.K., Chellappa, R.: ‘Matching shape sequences in video with applications in human movement analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (12), pp. 18961909.
    209. 209)
      • 209. Kusakunniran, W., Wu, Q., Li, H., et al: ‘Automatic gait recognition using weighted binary pattern on video’. IEEE Int. Conf. Advanced Video and Signal Based Surveillance, 2009, Genova, Italy, 2009, pp. 4954.
    210. 210)
      • 210. Huang, S., Elgammal, A., Huangfu, L., et al: ‘Globality-locality preserving projections for biometric data dimensionality reduction’. IEEE Conf. Computer Vision and Pattern Recogntion Workshops, Columbus, Ohio, USA, 2014, pp. 1520.
    211. 211)
      • 211. Huang, S., Elgammal, A., Lu, J., et al: ‘Cross-speed gait recognition using speed-invariant gait templates and globality–locality preserving projections’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (10), pp. 20712083.
    212. 212)
      • 212. Kusakunniran, W., Wu, Q., Zhang, J., et al: ‘Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model’, IEEE Trans. Syst. Man, Cybern, Part B (Cybern.), 2012, 42, (6), pp. 16541668.
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