Gait Recognition Based on Outermost Contour

Gait recognition aims to identify people by the way they walk. In this paper, a simple but effective gait recognition method based on outermost contour is proposed. For each gait image sequence, an adaptive silhouette extraction algorithm is firstly used to segment the frames of the sequence and a series of post-processing is applied to obtain the normalized silhouette images with less noise. Then a novel feature extraction method based on outermost contour is performed. Principal Component Analysis (PCA) is adopted to reduce the dimensionality of the distance signals derived from the outermost contours of silhouette images. Then Multiple Discriminant Analysis (MDA) is used to optimize the separability of gait features belonging to different classes. Nearest Neighbor (NN) classifier and Nearest Neighbor classifier with respect to class Exemplars (ENN) are used to classify the final feature vectors produced by MDA. In order to verify the effectiveness and robustness of our feature extraction algorithm, we also use two other classifiers - Backpropagation Neural Network (BPNN) and Support Vector Machine (SVM) for recognition. Experimental results on a gait database of 100 people show that the accuracy of using MDA, BPNN and SVM can achieve 97.67%, 94.33% and 94.67%, respectively.