Gender Classification Using a Novel Gait Template: Radon Transform of Mean Gait Energy Image

Any information about people such as their gender may be useful in some secure places; however, in some occasions, it is more appropriate to obtain such information in an unobtrusive manner such as using gait. In this study, we propose a novel method for gender classification using gait template, which is based on Radon Transform of Mean Gait Energy Image (RTMGEI). Robustness against image noises and reducing data dimensionality can be achieved by using Radon Transformation, as well as capturing variations of Mean Gait Energy Images (MGEIs) over their centers. Feature extraction is done by applying Zernike moments to RTMGEIs. Orthogonal property of Zernike moment basis functions guarantee the statistically independence of coefficients in extracted feature vectors. The obtained feature vectors are used to train a Support Vector Machine (SVM). Our method is evaluated on the CASIA database. The maximum Correct Classification Rate (CCR) of 98.94% was achieved for gender classification. Results show that our method outperforms the recently presented works due to its high performance.