LAUSR

In this paper, we propose an octagonal prism representation for local binary patterns (LBP). This representation implements a new circular distance measurement for face recognition under various illumination conditions. The LBP method has been widely used in many computer vision applications, particularly for face recognition. Most LBP matching methods use distribution features with a bin-to-bin distance measure. However, using this bin-to-bin distance measure may produce low similarity scores even for similar patterns. To address this problem, we placed the LBPs on an octagonal prism in a three dimensional space and used the Euclidean distance measure. In the proposed octagonal prism representation, the LBPs were represented as three dimensional vectors on the octagonal prism. Since similar patterns under different illumination conditions are located in the vicinity on the octagonal prism, the proposed method proved robust against illumination variations. The proposed method produced noticeably improved performance when using the CMU PIE, Yale B, and Extended Yale B databases.