View-invariant estimation of height and stride for gait recognition

We present a parametric method to automatically identify people in monocular low-resolution video by estimating the height and stride parameters of their walking gait. Stride parameters (stride length and cadence) are functions of body height, weight, and gender. Previous work has demonstrated effective use of these biometrics for identification and verification of people. In this paper, we show that performance is significantly improved by using height as an additional discriminant feature. Height is estimated by robustly segmenting the person from the background and fitting their apparent height to a time-dependent model. This method is correspondence-free and works with low-resolution images of people. It is also view-invariant, albeit performance is optimal in near fronto-parallel configurations. Identification accuracy is estimated at 47% for fronto-parallel sequences of 41 people, and 65% for non-fronto-parallel sequences of 17 people, compared with 18% and 51%, respectively, when only stride and cadence are used.