Gender Identification in Human Gait Using Neural Network

Автор: Richa Shukla, Reenu Shukla, Anupam Shukla, Sanjeev Sharma, Nirupama Tiwari

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 11 vol.4, 2012 года.

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Biometrics is an advanced way of person recognition as it establishes more direct and explicit link with humans than passwords, since biometrics use measurable physiological and behavioural features of a person. In this paper gender recognition from human gait in image sequence have been successfully investigated. Silhouette of 15 males and 15 females from the database collected from CASIR site have been extracted. The computer vision based gender classification is then carried out on the basis of standard deviation, centre of mass and height from head to toe using Feed Forward Back Propagation Network with TRAINLM as training functions, LEARNGD as adaptation learning function and MSEREG as performance function. Experimental results demonstrate that the present gender recognition system achieve recognition performance of 93.4%, 94.6%, and 94.7% with 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons respectively. When the performance function is replaced with SSE the recognition performance is increased by 2%, 2.4% and 3% respectively for 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons.The above study indicates that Gait based gender recognition is one of the best reliable biometric technology that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to quickly detect threats and provide differing levels of access to different user groups.

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Короткий адрес: https://sciup.org/15014505

IDR: 15014505

Список литературы Gender Identification in Human Gait Using Neural Network

  • 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,”First Workshops on Image Processing Theory, Tools and Applications, 2008, pp. 1-6.
  • J. Wang, M. She, S.Nahavandi, and A.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
  • B. Pogorelc and M. Gams, “Medically Driven Data Mining Application: Recognition of Health Problems from Gait Patterns of Elderly,”IEEE International Conference on Data Mining Workshops, 2010.
  • S. Ha, Y. Han and H. Hahn, “Adaptive Gait Pattern Generation of Biped Robot based on Human’s Gait Pattern Analysis,”World Academy of Science, Engineering and Technology, 34, 2007.
  • M. Hu, Y. Wang, Z. Zhang and Y. Wang, “Combining Spatial and Temporal Information for Gait Based Gender Classification,” International Conference on Pattern Recognition 2010.
  • X. Li, J. Stephen, M. S. Yan, D. Tao and D.Xu, “Gait Components and Their Application to Gender Recognition, IEEE Transactions On Systems, Man, And Cybernetics—Part C:”Applications And Reviews, 38(2),2008.
  • S. Yu, T. Tan, Kaiqi Huang, KuiJiaand Xinyu Wu, “A Study on Gait-Based Gender Classification,” IEEE Transactions On Image Processing, 18(8), 2009.
  • M.Hanmandlu, R.Bhupesh Gupta, F. Sayeed and A.Q. Ansari“An Experimental Study of different Features for Face Recognition,”International Conference on Communication Systems and Network Technologies, 2011.
  • R. Asmara, A.Basuki and K. Arai, “A Review of Chinese Academy of Sciences (CASIA) Gait Database As a Human Gait Recognition Dataset,” Industrial Electronics Seminar, 2011, Surabaya Indonesia.
  • J. P. Foster, M. S. Nixon andA. Prudel-Bennett, “Automatic gait recognition using area-based metrics,”Pattern Recognition Letters, 24, 2489–2497, 2003.
  • P. J. Phillips, S. Sarkar, I. Robledo, P. Grother and K. Bowyer, 2002. “The gait identification challenge problem: Data sets and baseline algorithm,”In:Proc. of 16th Internat. Conf. on Pattern Recognition, ICPR, 1, 385-388, 2002.
  • K. Kale, N. Cuntoor, B. Yegnanarayana, A. N. Rajagopalan and R. Chellappa, “Gait analysis for human identification,” In:Proc. of the 3rd Internat. Conf. on Audio and Video Based Person Authentication, 2003.
  • D. Ioannidis, D. Tzovaras, S. DamousisArgyropoulos, K. Moustakas, “Gait Recognition Using Compact Feature Extraction Transforms and Depth Information,”IEEE Trans Inf Forensics Security, 2(3), 623-630, 2007.
  • Golomb, B.A., Lawrence, D.T., Sejnowski, T.J., 1991. SexNet: A neural network identifies sex from human faces. NIPS 3 In:Proc. of Internat. Conf. Advances in Neural Information Processing Systems, NIPS 3, Vol . 3, pp.572-577.
  • B. Moghaddam, M. H. Yang, 2002. “Learning gender with support faces,”IEEE Trans Pattern Anal Mach Intell., (PAMI), 24(5), 707–711, 2002.
  • Y. Andreu, P. Garc´ıa-Sevilla, A. Ram´on, E. Mollineda, 2009. “Dealing with Inaccurate Face Detection for Automatic Gender Recognition with Partially Occluded Faces,”In:Proc. of the 14th Iberoamerican Conf. on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications,Bayro-Corrochano and J.-O. Eklundh (Eds.), CIARP 2009, LNCS 5856, Springer-Verlag Berlin Heidelberg.5856, 749-757, 2009.
  • H. Harb, L. Chen, 2003. “Gender identification using a general audio classifier,”In:Proc. of Internat. Conf. on Multimedia and Expo, ICME’03,” 1, 733-736, 2003. Washington, DC, USA.
  • X. Li, S. J. Maybank, S. Yan, D. Tao and D. Xu, “Gait components and their application to gender recognition,”IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 38(2), 145–155, 2008.
  • A. P.Yazdanpanah, K. Faez, R. Amirfattahi, 2010. “Multimodal Biometric System Using Face, Ear And Gait Biometrics,”In: Proc. of 10th Internat. Conf. on Information Science, Signal Processing and their Applications, ISSPA, 251-254, 2010.
  • J. H. Na, M. S. Park and J. Y. Choi, “Pre-clustered principal component analysis for fast training of new face databases,” In: Proc. of Internat. Conf. on Control, Automton. Syst., ICCAS”07, 1144-1149, 2007.
  • S. E. Umbaugh, Y. Wei and M. Zuke,“Feature extraction in image analysis”. IEEE Eng Med Bio Soc., 16(4), 62-73, 1997.
  • [http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp].
  • A. Ross, A. Jain, “Information fusion in biometrics,”Pattern Recognition Letters, 24(13), 2115–2125, 2003.
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