Face recognition using adaptive local directional pattern

Robust facial representation approach is critical for face recognition. LDP is a more stable robust descriptor using gradient direction instead of intensity value. But it is less precise to treat the directional response values in the same way and it does not obtain enough information only considering fixed absolute values of the edge responses. we propose an adaptive local directional pattern (ALDP) feature descriptor for face recognition in this paper. Positive and negative edge directions are extracted to explore more valuable discriminant information in our ALDP. Based on Weber's law, an automatic threshold setting strategy is proposed to make the ALDP codes flexible and precise. The experiment results indicate our ALDP has higher recognition accuracy in comparison with traditional methods.