A study on gait entropy image analysis for clothing invariant human identification
A simple and common human gait may be viewed as a strong biometric cue to solve human identification problem through understanding the intrinsic patterns of gait biometrics. An individual's gait pattern appears to be different in gallery and probe gait sequences due to wearing dissimilar clothing types. The gait dataset captures the possible changes found in silhouette shape image which provides the difficulty in distinguishing among individuals. In this paper, a robust feature selection technique has been addressed through Gait Entropy Image (GEnI) analysis. The GEnI has the capacity to accumulate most significant motion information. The width of GEnI, along the horizontal axis is taken as discriminative feature which produces a small intra class variance. This information is studied as an evidence of feature invariance. The standard statistical tests such as pair wise clothing correlation and intra clothing variance are performed on gait dataset to evaluate the reliability of feature. Experimental results demonstrate the efficiency of proposed feature selection method using k nearest neighbor (k NN) minimum distance classifier (MDC) and support vector machine (SVM) algorithms. The performance analysis of recognition system has been evaluated on OU ISIR Treadmill B gait database with different error metrics after performing N fold cross validation method.
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