Recognizing Night Walkers Based on One Pseudoshape Representation of Gait

Gait is a promising biometric cue which can facilitate the recognition of human beings, particularly when other biometrics are unavailable. Existing work for gait recognition, however, lays more emphasis on the problem of daytime walker recognition and overlooks the significance of walker recognition at night. This paper deals with the problem of recognizing nighttime walkers. We take advantage of infrared gait patterns to accomplish this task: 1) Walker detection is improved using intensity compensation-based background subtraction; 2) pseudoshape-based features are proposed to describe gait patterns; 3) the dimension of gait features is reduced through the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques; 4) temporal cues are exploited in the form of the relevant component analysis (RCA) learning; 5) the nearest neighbor classifier is used to recognize unknown gait. Experimental results justify the effectiveness of our method and show that our method has an encouraging potential for the application in surveillance systems.