Human identification using gait

Gait refers to the style of walking of an individual. This paper presents a view-invariant approach for human identification at a distance, using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. Based on principal component analysis (PCA), this paper describes a simple, but efficient approach to gait recognition. Binarized silhouettes of a motion object are represented by 1-D signals, which are the basic image features called distance vectors. The distance vectors are differences between the bounding box and silhouette, and are extracted using 4 projections of the silhouette. Based on normalized correlation of the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, eigenspace transformation, based on PCA, is applied to time-varying distance vectors and Mahalanobis distances-based supervised pattern classification are then performed in the lower-dimensional eigenspace for human identification. A fusion strategy is finally executed to produce a final decision. Experimental results on 3 main databases demonstrate that the right person in the top 2 matches 100% of the time for the cases where training and testing sets corresponds to the same walking styles, and in the top 3-4 matches 100% of the time when training and testing sets do not correspond to the same walking styles. © TÜBTAK.