A New Gait Representation for Human Identification: Mass Vector

Gait is a new biometric aimed to recognize individuals by the way they walk. Gait recognition has recently an increasing interest from researchers due to several advantages. In this paper, we have proposed a new representation for human gait recognition which is called as mass vector. The mass vector along a given row is defined as the number of pixels with a nonzero value in a given row of the binarized silhouette of a walking person. Sequences of temporally ordered mass vector are used to represent a gait of an individual. We use the dynamic time-warping (DTW) approach for matching so that non-linear time normalization may be used to deal with the naturally-occurring changes in walking speed. Experimental results show that mass vector has a high discriminative power for gait recognition. The recognition rate is around 96.25% in a canonical viewing angle in NLPR gait database by using mass vector. Our proposed system outperforms previous works.