Local binary pattern on hexagonal structure for face matching

Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007, 2007, pp. 455 - 460
Issue Date:
2007-12-01
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Principal Components Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA), have been widely used for 2D face recognition. Local Binary Pattern (LBP), however, provides a simpler and more effective way to represent faces. With LBP, face image is divided into small regions from which LBP histograms are extracted and concatenated into a single and global feature histogram representing the face image. The recognition is performed using Chi square and other commonly used dissimilarity measures. In this paper, we construct LBP codes together with three dissimilarity measures on hexagonal structure. We show that LBPs defined on hexagonal structure will lead to a faster and more accurate scheme for face recognition.
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