Face recognition using Multi-modal Binary Patterns

A new descriptor called Multi-modal Binary Patterns (MMBP) is proposed for face recognition. It balances well important requirements for real-world applications, including the robustness, discriminative power, and the low computational cost. The proposed algorithm has several desirable properties: 1) it captures information from face image in any direction as it is oriented feature, 2) being a spatial multi-scale structure, the descriptor catches not only local but also more global information about object, 3) it is robust to image transformation like variations of lighting, expressions, and 4) it is computationally efficient. In more detail, to catch information in a given direction, a Local Line Binary Pattern (LLBP) based operator is first applied. The MMBP feature is then built by applying a LBP-based self-similarity operator on the values being calculated by LLBP operators across different directions. A Whitened PCA dimensionality reduction technique is applied to get more a compact and efficient descriptor. Experimental results achieved on the comprehensive FERET data set being comparable to state-of-the-art validates the efficiency of our method.