Abstract
Soft biometrics-based gender classification is an interesting and a challenging area of neural networking and has potential application in visual surveillance as well as human–computer interaction. In this paper, we have investigated gender recognition from human gait in image sequence. For the above purpose, we have extracted silhouette of 15 males and 15 females from the database collected from CASIA Gait Database (Dataset B). The computer-vision-based gender classification is then carried out on the basis of standard deviation, center of mass, and height from head to toe. Experimental results demonstrate that the present gender recognition systems achieve superior recognition performance of 96.8 % on feed-forward back-propagation (FFBP) network. Data on different networks have also been trained and tested. The above study indicates that gait-based gender recognition is one of the best reliable biometric technologies 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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Shukla, R., Shukla, R., Shukla, A., Tiwari, N. (2014). Gender Identification Using Gait Biometrics. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_18
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DOI: https://doi.org/10.1007/978-81-322-1665-0_18
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