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The Chinese Journal of Artificial Intelligence


ISSN (Print): 2666-7827
ISSN (Online): 2666-7835

Research Article

Robust and Lightweight System for Gait-based Age Estimation towards Viewing Angle Variations

Author(s): Jaychand Upadhyay*, Tad Gonsalves and Vijay Katkar

Volume 1, 2022

Published on: 06 September, 2022

Article ID: e260822208023 Pages: 10

DOI: 10.2174/2666782701666220826104925


Background: In computer vision applications, gait-based age estimation across several cameras is critical, especially when following the same person from various viewpoints.

Introduction: Gait-based age recognition is a very challenging task as it involves multiple hurdles, such as a change in the viewpoint of the person. The proposed system handles this problem by performing a sequence of tasks, such as GEI formation from silhouette, applying DCT on GEI and extracting the features and finally using MLP for age estimation. The proposed system proves its effectiveness by comparing the performance with state-of-the-art methods, conventional methods and deep learning-based methods. The performance of the system is estimated on OU-MVLP and OULP-Age datasets. The experimental results show the robustness of the system against viewing angle variations.

Objective: This study aimed to implement the system, which adopts a lightweight approach for gaitbased age estimation.

Methods: The proposed system uses a combination of the discrete cosine transform (DCT) and multilayer perceptron (MLP) on gait energy image (GEI) to perform age estimation.

Results: The performance of the system is extensively evaluated on the OU-MVLP and OULP-Age datasets.

Conclusion: The proposed system attains the best mean absolute error (MAE) of 5.05 (in years) for the OU-MVLP dataset and 5.65 for the OULP dataset.

Keywords: Gait, age estimation, GEI, OU-MVLP, OULP, DCT.

Graphical Abstract
Ghosh, R. Centre-of-mass based gait recognition for person identification. Rec Adv Comp Sci Commun, 2021, 14(6), 1749-1757.
Yu, S.; Tan, D.; Tan, T. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition.18th International Conference on Pattern Recognition (ICPR’06); IEEE: Hong Kong, China , 2006, pp. 441-444.
Stevenage, S.V.; Nixon, M.S.; Vince, K. Visual analysis of gait as a cue to identity. Appl. Cogn. Psychol., 1999, 13(6), 513-526.
Makihara, Y.; Sagawa, R.; Mukaigawa, Y.; Echigo, T.; Yagi, Y. Gait recognition using a view transformation model in the frequency do-main.Proceedings of the 9th European conference on Computer Vision - Volume Part III; Springer: Berlin, Heidelberg, 2006, pp. 151-163.
Sarkar, S.; Phillips, P.J.; Liu, Z.; Vega, I.R.; Grother, P.; Bowyer, K.W. The humanID gait challenge problem: Data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(2), 162-177.
[] [PMID: 15688555]
Lu, J.; Tan, Y.P. Ordinary preserving manifold analysis for human age and head pose estimation. IEEE Trans. Hum. Mach. Syst., 2013, 43(2), 249-258.
Lu, J.; Tan, Y.P. Ordinary preserving manifold analysis for human age estimation.IEEE Computer Society and IEEE Biometrics Council Workshop on Biometrics; IEEE: San Francisco, CA, USA, 2010, pp. 1-6.
Lu, J.; Tan, Y.P. Gait-based human age estimation. IEEE Trans. Inf. Forensics Security, 2010, 5(4), 761-770.
Davis, J. Visual categorization of children and adult walking styles.Proceedings of the Third International Conference on Audio and Video Based Biometric Person Authentication; Springer: Berlin, Heidelberg, 2001, pp. 295-300.
Begg, R.K.; Palaniswami, M.; Owen, B. Support vector machines for automated gait classification. IEEE Trans. Biomed. Eng., 2005, 52(5), 828-838.
[] [PMID: 15887532]
Makihara, Y.; Okumura, M.; Iwama, H.; Yagi, Y. Gait-based age estimation using a whole-generation gait database.2011 International Joint Conference on Biometrics (IJCB); IEEE: Washington, DC, USA, 2011, pp. 1-6.
Xuelong, Li Maybank, S.J.; Shuicheng Yan; Dacheng Tao; Dong Xu, Gait components and their application to gender recognition. IEEE Trans. Syst. Man Cybern. C, 2008, 38(2), 145-155.
Shiqi, Y.; Tieniu, T.; Kaiqi, H.; Kui, Jia Xinyu Wu, A study on gait-based gender classification. IEEE Trans. Image Process., 2009, 18(8), 1905-1910.
[] [PMID: 19447706]
Lemke, M.R.; Wendorff, T.; Mieth, B.; Buhl, K.; Linnemann, M. Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls. J. Psychiatr. Res., 2000, 34(4-5), 277-283.
[] [PMID: 11104839]
Li, X.; Makihara, Y.; Xu, C.; Yagi, Y.; Ren, M. Gait-based human age estimation using age group-dependent manifold learning and re-gression. Multimedia Tools Appl., 2018, 77(21), 28333-28354.
Mannami, H.; Makihara, Y.; Yagi, Y. Gait analysis of gender and age using a large-scale multi-view gait database. Computer Vision - ACCV 2010 - 10th Asian Conference on Computer Vision, 2010, pp. 975-986.
Smola, A.J.; Schölkopf, B. A tutorial on support vector regression. Stat. Comput., 2004, 14(3), 199-222.
Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A discriminant analysis for under sampled data. COLT ’92: Proceedings of the fifth annual workshop on Computational Learning Theory, 1992, pp. 144-152.
Marn-Jimnez, M.J.; Castro, F.M.; Guil, N.; De La Torre, F.; Medina-Carnicer, R. Deep multi-task learning for gait-based biometrics.2017 IEEE International Conference on Image Processing (ICIP); IEEE: Beijing, China, 2017, pp. 106-110.
Zhang, S.; Wang, Y.; Li, A. Gait-based age estimation with deep convolutional neural network.2019 International Conference on Biomet-rics (ICB); IEEE: Crete, Greece, 2019, pp. 1-8.
Li, X.; Makihara, Y.; Xu, C.; Yagi, Y.; Ren, M. Make the bag disappear: Carrying status- invariant gait-based human age estimation using parallel generative adversarial networks.IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS); IEEE: Tampa, FL, USA, 2019, pp. 1-9.
Xu, C.; Makihara, Y.; Ogi, G.; Li, X.; Yagi, Y.; Lu, J. The OU-ISIR gait database comprising the large population dataset with age and performance evaluation of age estimation. IPSJ Transactions on Computer Vision and Applications, 2017, 9(1), 24-24.
Sakata, A.; Makihara, Y.; Takemura, N.; Muramatsu, D.; Yagi, Y. Gait based age estimation using a dense net. Computer Vision – ACCV 2018 Workshops, 2018, 55-63.
Xu, C.; Sakata, A.; Makihara, Y.; Takemura, N.; Muramatsu, D.; Yagi, Y.; Lu, J. Uncertainty-aware gait-based age estimation and its applications. IEEE Trans. Biometrics Behav. Identity Sci., 2021, 3(4), 479-494.
Xu, C.; Makihara, Y.; Liao, R.; Niitsuma, H.; Li, X.; Yagi, Y.; Lu, J. Real-time gait-based age estimation and gender classification from a single image.IEEE Winter Conference on Applications of Computer Vision (WACV); IEEE: Waikoloa, HI, USA, 2021, pp. 3459-3469.
Chuen, B.K.Y.; Connie, T.; Song, O.T.; Goh, M. A preliminary study of gait-based age estimation techniques.Asia-pacific signal and information processing association annual summit and conference (APSIPA); Hong Kong, China, IEEE, 2015, pp. 800-806.
Nabila, M.; Mohammed, A.I.; Yousra, B.J. Gait‐based human age classification using a silhouette model. IET Biom., 2018, 7(2), 116-124.
Mansouri, N.; Aouled, I.M.; Ben Jemaa, Y. Gait features fusion for efficient automatic age classification. IET Comput. Vis., 2018, 12(1), 69-75.
Abirami, B.; Subashini, T.S.; Mahavaishnavi, V. Automatic age-group estimation from gait energy images. Mater. Today Proc., 2020, 33, 4646-4649.
Aderinola, T.B.; Connie, T.; Ong, T.S.; Yau, W.C.; Teoh, A.B.J. Learning age from gait: A survey. IEEE Access, 2021, 9, 100352-100368.
Riaz, Q.; Hashmi, M.Z.U.H.; Hashmi, M.A.; Shahzad, M.; Errami, H.; Weber, A. Move your body: Age estimation based on chest movement during normal walk. IEEE Access, 2019, 7, 28510-28524.
Passos, W.L.; Araujo, G.M.; Gois, J.N.; de Lima, A.A. A gait energy image-based system for Brazilian sign language recognition. IEEE Trans. Circuits Syst. I Regul. Pap., 2021, 68(11), 4761-4771.
Han, J.; Bhanu, B. Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28(2), 316-322.
[] [PMID: 16468626]
Ahmed, N.; Natarajan, T.; Rao, K.R. Discrete cosine transform. IEEE Trans. Comput., 1974, C-23(1), 90-93.
Hemachandran, K.; Justus Rabi, B. Performance analysis of discrete cosine transform and discrete wavelet transform for image com-pression. J. Eng. Appl. Sci. (Asian Res. Publ. Netw.), 2018, 13(2), 436-440.
Chen, J.; Liu, S.; Deng, G.; Rahardja, S. Hardware efficient integer discrete cosine transform for efficient image/video compression. IEEE Access, 2019, 7, 152635-152645.
Pennebaker, W.B.; Mitchell, J.L. JPEG: Still Image Data Compression Standard, 1st ed; Kluwer Academic Publishers: Norwell, 1992.
Bentahar, A.; Meraoumia, A.; Bendjenna, H. IoT securing system using fuzzy commitment for DCT-based fingerprint recognition. 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), 2018, pp. 1-5.
Alkhateeb, J.; Ren, J.; Jiang, J.; Ipson, S.S.; El Abed, H. Word-based handwritten Arabic scripts recognition using DCT features and neu-ral network classifier; IEEE Xplore, 2008, pp. 1-5.
Kohir, V.V.; Desai, U. Face recognition using a DCT-HMM approach.Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV’98 (Cat. No.98EX201); IEEE, Princeton: NJ, USA, 1998, pp. 226-231.
Tsai, M.J.; Hung, H.Y. DCT and DWT-based image watermarking by using subsampling.24th International Conference on Distributed Computing Systems Workshops; 2004. Proceedings, IEEE: Tokyo, Japan, 2004, pp. 184-189.
Al-Haj, A. Combined DWT-DCT digital image watermarking. J. Comput. Sci., 2007, 3(9), 740-746.
Takemura, N.; Makihara, Y.; Muramatsu, D.; Echigo, T.; Yagi, Y. Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comp. Vis. Appl., 2018, 10(1), 4-4.
Sakata, A.; Takemura, N.; Yagi, Y. Gait-based age estimation using multi-stage convolutional neural network. IPSJ Trans. Comp. Vis. Appl., 2019, 11(1), 4-4.
Shiraga, K.; Makihara, Y.; Muramatsu, D.; Echigo, T.; Yagi, Y. GEINet: View-invariant gait recognition using a convolutional neural network.International Conference on Biometrics (ICB); IEEE: Halmstad, Sweden, 2016, pp. 1-8.
Yoo, H.W.; Kwon, K.Y. Method for classification of age and gender using gait recognition. Trans. Korean Soc. Mech. Eng. A., 2017, 41(11), 1035-1045.
Punyani, P.; Gupta, R.; Kumar, A. A comparison study of face, gait and speech features for age estimation.Advances in Electronics, Communication and Computing, 1st ed; Kalam, A.; Das, S.; Shar-ma, K., Eds.; Springer: Singapore, 2018, Vol. 443, pp. 325-331.
Hema, M.; Pitta, S. Human age classification based on gait parameters using a gait energy image projection model. 3rd International Con-ference on Trends in Electronics and Informatics (ICOEI); IEEE: Tirunelveli, India, 2019, pp. 1163-1168.
Aderinola, T.B.; Connie, T.; Ong, T.S.; Goh, K.O.M. Automatic extraction of spatio temporal gait features for age group classification.Proceedings of International Conference on Innovations in Information and Communication Technologies; Springer: Singapore, 2021, pp. 71-78.

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