Discriminative local binary patterns for human detection in personal album

In recent years, local pattern based object detection and recognition have attracted increasing interest in computer vision research community. However, to our best knowledge no previous work has focused on utilizing local patterns for the task of human detection. In this paper we develop a novel human detection system in personal albums based on LBP (local binary pattern) descriptor. Firstly we review the existing gradient based local features widely used in human detection, analyze their limitations and argue that LBP is more discriminative. Secondly, original LBP descriptor does not suit the human detecting problem well due to its high complexity and lack of semantic consistency, thus we propose two variants of LBP: Semantic-LBP and Fourier-LBP. Carefully designed experiments demonstrate the superiority of LBP over other traditional features for human detection. Especially we adopt a random ensemble algorithm for better comparison between different descriptors. All experiments are conducted on INRIA human database.

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