Cloth invariant gait recognition using pooled segmented statistical features

Abstract

A natural and normal gait can be used as a biometric cue in finding a solution to the human identification problem. An individual׳s appearance is likely to change with the variation in different clothes which further compounds the problem of gait identification. The clothing differences between gallery and probe datasets capture the possible changes in their silhouette׳s shape which increases the inability to discriminate between individuals. In this paper, an attempt has been made to provide a novel statistical shape analysis method based on Gait Energy Image (GEI) which is decomposed into three independent shape segmentations such as horizontal, vertical and grid resolution. The pooled segmented statistical features describe the shape of the GEI edge contour. The higher order moments about the shape centroid are likely to be invariant to small changes in silhouette shape. They implicitly describe the underlying distribution of the shape and can be used in conjunction with a set of other area based features to increase the efficacy of the classification results. The features reliability test has been performed with three classical statistical methods such as intra cloths variance (F-Statistics), inter subject distance (t-Statistics) and Intra-Class Correlation (ICC) on each set of segment of features. This analysis illustrates that combination of features holds less discrimination in comparison to grid based shape segmentation for different clothes. The similarity measurement comprises of different classification techniques (k-Nearest Neighbor, Naïve Bayes׳, Decision Tree (C4.5) and Random Forest) to produce acceptable recognition results on OU-ISIR dataset. The degree of discriminability of these classifiers has been measured by statistical metrics such as F1-Score, Precision, Recall, and ROC curve.