Gait Identification Using a Novel Gait Representation: Radon Transform of Mean Gait Energy Image

Gait is one of the most practical biometric techniques which present the capability to recognize individuals from distance. In this study, we propose a novel gait template based on Radon Transform of Mean Gait Energy Image, as RTMGEI. Robustness against image noises and reducing data dimensionality can be achieved by using Radon Transform, as well as capturing variations of Mean Gait Energy Images (MGEIs) over their centers. Feature extraction is done by applying the Zernike moments to RTMGEIs. Orthogonal property of Zernike moment basis functions guarantees the statistically independence of coefficients in extracted feature vectors. The Euclidean minimum distance is used as the classifier. The our proposed method is evaluated on the CASIA database. Results show that our method outperforms recently presented works due to its high performance.