Research on a gait recognition algorithm based on generalized principal component analysis

TL;DR: Effective segmentation using a simple method to extract silhouettes of walking figures from the background was the first step of the method; this played a key role in gait recognition.

Abstract: Gait recognition makes use of human walking patterns for biometric recognition and is a novel topic in the field.Effective segmentation using a simple method to extract silhouettes of walking figures from the background was the first step of our method;this played a key role in gait recognition.Then,morphology was used and a standardized and centralized image was obtained by geometric transformation.Afterwards gait energy image(GEI) was used as a feature extraction method——describing gait characteristics obtained using periodic sequence images according to their cyclical divisions.Following this,feature dimensionality was reduced through principal component analysis(PCA),two-dimensional principal component analysis(2DPCA),complete two-dimensional principal component analysis(C2DPCA) and weighted complete two-dimensional principal component analysis(WC2DPCA) respectively.The nearest neighbor classifier was then used to distinguish different human gaits.By balancing calculation and recognition rates,experimental results demonstrated that 2DPCA based on GEI has encouraging recognition performance with a recognition rate of about 95.43%.