Weighted Local Binary Pattern Infrared Face Recognition Based on Weber's Law

The traditional LBP Histogram representation extracts the local micro-patterns and assigns the same weight all local micro-patterns. To combine the different contribution to face recognition, this paper proposes a weighted LBP histogram based on Weber's law. Firstly, inspired by psychological Weber's law, intensity of local micro-pattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors, the other is intensity of local central pixel. Secondly, regarding the intensity of local micro-pattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and uniform patterns are applied to get final features. The experiment results demonstrate that the proposed method outperforms the methods based on traditional LBP.

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