A Brief Literature Review of some Efficient Human Gait Analysis Based Gender Classification Techniques
Автор: Satyam Rawat
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 3 vol.14, 2022 года.
Бесплатный доступ
Gait based gender classification is an emerging area in the field of biometrics that has received a lot of interest from researchers mainly due to its advantages over the other methods and its potential application. Gait based gender classification helps a vision based biometric analysis system by focusing the gender-unique features. This helps to improves the performance of the model by limiting the authentication database searching to only one gender. Through the years, researchers have tried a wide variety of techniques and their combinations to improve the accuracy of gait based biometric systems in varying use-cases. In this study, we have given a brief overview of some of the recent and pioneering works done in the field of gait-based gender classification.
Gait, Biometrics, classification, gender, Gait cycle
Короткий адрес: https://sciup.org/15018433
IDR: 15018433 | DOI: 10.5815/ijieeb.2022.03.05
Список литературы A Brief Literature Review of some Efficient Human Gait Analysis Based Gender Classification Techniques
- X. Qinghan, “Technology review – Biometrics Technology, Application, Challenge, and Computational Intelligence Solutions”, IEEE Computational Intelligence Magazine, vol. 2, pp. 5-25, 2007.
- Jin Wang, Mary She, Saeid Nahavandi, Abbas Kouzani, “A Review of Vision-based Gait Recognition Methods for Human Identification”, IEEE Computer Society, 2010 International Con-ference on Digital Image Computing: Techniques and Applications, pp. 320 - 327, 2010
- N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, “Gait recognition: a challenging signal processing technology for biometric identification”, IEEE Signal Processing Magazine, vol. 22, pp. 78-90, 2005.
- M. S. Nixon and J. N. Carter, "Automatic Recognition by Gait", Proceedings of the IEEE, vol. 94, pp. 2013-2024, 2006.
- Y. Jang-Hee, H. Doosung, M. Ki-Young, and M. S. Nixon, “Automated Human Recognition by Gait using Neural Network”, in First Workshops on Image Processing Theory, Tools and Applications, 2008, pp. 1-6
- Tarun Choubisa, Mohan Kashyap, Rithesh R N, Sampad B. Mohanty, “Direction and Gender Classification Using Convolutional Neural Network for Side-view Images Captured from a Monitored Trail,” Indian Institute of Science, Bengaluru- 978-1-5090-6734-3/17/$31.00 2017 IEEE
- N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, “Gait recognition: a challenging signal processing technology for biometric identification”, IEEE Signal Processing Magazine, vol. 22, pp. 78-90, 2005
- N. F. Troje, “Decomposing biological motion: A framework for analysis and synthesis of human gait patterns,” J. Vis., vol. 2, no. 5, pp. 371–387, 2002
- K. Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In NIPS, 2005.
- Edward WONG Kie Yih, G. Sainarayanan, Ali Chekima, "Palmprint Based Biometric System: A Comparative Study on Discrete Cosine Transform Energy, Wavelet Transform Energy and SobelCode Methods", Biomedical Soft Computing and Human Sciences, Vol.14, No.1, pp.11- 19, 2009
- Dong Xu, Shuicheng Yan, Dacheng Tao, Stephen Lin, and Hong-Jiang, Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval, IEEE Transactions On Image Processing, Vol. 16, No. 11, November 2007
- Mohammed Hussein Ahmed, Azhin Tahir Sabir, “Human Gender Classification based on Gait Features using Kinect Sensor,” Koya University Kurdistan Region of Iraq- 978-1-5386-2201-8/17/$31.00 2017 IEEE
- Mustafa Eren Yildirim1,Omer Faruk Ince, Ibrahim Furkan Ince, JangSik Park, Byung-Woo Yoon, “Application of Maximized Inter-class Variance for Gender Classification using RGB-Depth Camera,” Bahcesehir University, Istanbul, Turkey - 978-89-93215-13-7/17/$31.00 2017 ICROS
- Lei Cai, Huanqiang Zeng, Jianqing Zhu, Jiuwen Cao, Junhui Hou, and Canhui Cai, “Multi-View Joint Learning Network For Pedestrian Gender Classification,” The City University of Hong Kong.- 978-1-5386-2159- 2/$31.00 2017 IEEE
- Maodi hu, Y. wang, Z. Zhang and D. Zhang,” Gait-based gender classification using mixed conditional random field”, IEEE transactions on systems, man and cybernetics-Part B: Cybernetics 41(5):1429-1439,2011
- [Zhang De, Research on Gait based Gender Classification via Fusion of Multiple Views, International Database of Database Theory and Application,2015
- Sneha Choudhary, Chandra Prakash, and Rajesh Kumar, “A Hybrid Approach for Gait based Gender Classification using GEI and Spatio Temporal parameters,” 978-1-5090-6367-3/17/$31.00 2017 IEEE
- Xuelong Li, Stephen J. Maybank, Shuicheng Yan, Dacheng Tao, and Dong Xu, Gait Components and Their Application to Gender Recognition, IEEE Transactions On Systems, Man, And Cybernetics— Part C: Applications And Reviews, Vol. 38, No. 2, March 2008
- Shiqi Yu, , Tieniu , Kaiqi Huang, Kui Jia, Xinyu Wu, A Study on Gait-Based Gender Classification, IEEE Transactions On Image Processing, Vol. 18, No. 8, August 2009
- M.Hanmandlu, R.Bhupesh Gupta, Farrukh Sayeed, A.Q.Ansari, An Experimental Study of different Features for Face Recognition, International Conference on Communication Systems and Network Technologies, 2011
- Rosa Asmara, Achmad Basuki, Kohei Arai, A Review of Chinese Academy of Sciences (CASIA) Gait Database As a Human Gait Recognition Dataset, published in the Industrial Electronics Seminar 2011, Surabaya Indonesia
- Suvarna S., Shah, K., “LITERATURE REVIEW: MODEL FREE HUMAN GAIT RECOGNITION”, 978-1-4799-3070-8/14 $31.00 © 2014 IEEE
- Nidhi M. Bora, Gajendra V. Molke, Hemant R. Munot, “Understanding Human Gait: A Survey of Traits for Biometrics and Biomedical Applications”, 5 International Conference on Energy Systems and Applications,2015
- J. B. dec. M. Saunders, V. T. Inman and H. D. Eberhart, “The Major Determinants in Normal and Pathological Gait,” The Journal of Bone and Joint Surgery, Vol. 35-A, No. 3, 1953, pp. 543-558.
- Jeffrey E. Boyd, James J. Little, “Biometric Gait Recognition”, Springer-Verlag Berlin Heidelberg, pp. 19–42, 2005
- J. H. Yoo., “Feature Extraction and Selection for Recognizing Humans by Their Gait”, Springer-Verlag Berlin Heidelberg 2006.
- L. T. Kozlowski and J. E. Cutting, “Recognizing the sex of a walker from a dynamic point-light display,” Percpt. Psychophys., vol. 21, pp. 575–580, 1977.
- J. W. Davis and H. Gao, “Gender recognition from walking movements using adaptive three-mode PCA,” in Proc. Conf. Computer Vision and Pattern Recognition Workshop, Washington, DC, 2004, vol. 1, p. 9.
- L. R. Sudha & R. Bhavani (2013) AN EFFICIENT SPATIO-TEMPORAL GAIT REPRESENTATION FOR GENDER CLASSIFICATION, Applied Artificial Intelligence, 27:1, 62-75, DOI: 10.1080/08839514.2013.747373
- Bogdan Pogorelc, Matjaž Gams, Medically Driven Data Mining Application: Recognition of Health Problems from Gait Patterns of Elderly, IEEE International Conference on Data Mining Workshops, 2010
- Seungsuk Ha, Youngjoon Han, Hernsoo Hahn, Adaptive Gait Pattern Generation of Biped Robot based on Human’s Gait Pattern Analysis, World Academy of Science, Engineering and Technology 34 2007
- Jiwen Lu, 1, Gang Wang, Thomas S. Huang, “Gait-Based Gender Classification in Unconstrained Environments”, 21st International Conference on Pattern Recognition, 2012
- Chandrakant P. Divate, Dr. Syed Zakir Ali, “Study of Different Bio-metric Based Gender Classification Systems”, Proceedings of the International Conference on Inventive Research in Computing Applications,2018
- K. Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. In NIPS, 2005.
- J. Davis, B. Kulis, P. Jain, S. Sra, and I. Dhillon. Information-theoretic metric learning. In ICML, pages 209–216, 2007
- Maodi Hu, Yunhong Wang, Zhaoxiang Zhang, Yiding Wang, “Combining Spatial and Temporal Information for Gait Based Gender Classification”, International Conference on Pattern Recognition, 2010
- L.Lee and W.E.L.Grimson. Gait analysis for recognition and classification. In AFGR, pages 148–155, 2002. [6] L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE, 77(7):1169–1179, July 1988
- G. Huang and Y. Wang. Gender classification based on fusion of multi-view gait sequences. In ACCV, pages 462–471, 2007
- G. Antipov, S. Berrani, N. Ruchaud, and J. Dugelay, “Learned vs. hand-crafted features for pedestrian gender recognition,” in Proceedings of the 23th ACM International conference on Multimedia, 2015, pp. 1263–1266.
- K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
- C. Szegedy, W. Liu, and Y. Jia, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.
- L. Cai, J. Zhu, H. Zeng, J. Chen, C. Cai, and K. K Ma, “Hog-assisted deep feature learning for pedestrian gender recognition,” Journal of the Franklin Institute, 2017, doi: 10.1016/j.jfranklin.2017.09.003.