Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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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 images and a series of postprocessing is applied to the silhouette images to obtain the normalized silhouettes with less noise. Then a novel feature extraction method based on Outermost Contour is proposed. Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) are adopted to reduce the dimensionality of the feature vectors and to optimize the class separability of different gait image sequences simultaneously. Two simple pattern classification methods are used on the low-dimensional eigenspace for recognition. Experimental results on a gait database of 100 people show that the accuracy of our algorithm achieves 97.67%.