Abstract
In gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one gait cycle. However, in reality, there might be available only a few frames of full gait cycle of a subject due to occlusion. Therefore, gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a gait recognition algorithm from an incomplete gait cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public gait dataset demonstrate the validity of the proposed method.
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