A MANOVA of Major Factors of RIU-LBP Feature for Face Recognition

Local Binary Patterns (LBP) feature is one of the most popular representation schemes for face recognition. The four factors deciding its effect are the blocking number, image resolution, the sampling radius and sampling density of LBP operator. Numerous previous researches have taken various groups of value of these factors based on experimental comparisons. However, which factor among them contributes the most? Numerous revisions are made to the LBP operators for it is believed that the LBP coding is the most essential factor. Is it true? In this paper, with the very simple and classical Multivariate Analysis of Variance (MANOVA), we discover that the blocking number contributes the most; though all four factors have significant effect for recognition rate. In addition, with the same analysis, we disclose the detailed effect of each factor and their interactions to the precision of LBP features.