Gender recognition from gait using radon transform and relevant component analysis

Authors:
Lei Chen;Yunhong Wang;Yiding Wang;De Zhang
Affiliations:
School of Computer Science and Engineering, Beihang University, Beijing, China;School of Computer Science and Engineering, Beihang University, Beijing, China;College of Information Engineering, North China University of Technology, Beijing;School of Computer Science and Engineering, Beihang University, Beijing, China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

In this paper, a new method for gender recognition via gait silhouettes is proposed. In the feature extraction process, Radon transform on all the 180 angle degrees is applied to every silhouette to construct gait templates and the initial phase of each silhouette in an entire gait cycle is also associated to the templates representing dynamic information of walking. Then the Relevant Component Analysis (RCA) algorithm is employed on the radon-transformed templates to get a maximum likelihood estimation of the within class covariance matrix. At last, the Mahalanobis distances are calculated to measure gender dissimilarity in recognition. The Nearest Neighbor (NN) classifier is adopted to determine whether a sample in the Probe Set is male or female. Experimental results in comparison to state-of-the-art methods show considerable improvement in recognition performance of our proposed algorithm.