MMI-based optimal LBP code selection for facial expression recognition

Many variants of local binary patterns (LBPs) are widely used for face analysis due to their inherent simplicity and robustness. However, it has not yet been proven that LBPs are optimal for this task in regards to achieving the best balance between minimizing code numbers and reducing classification error. We propose an effective code selection method for selecting optimal LBP (OLBP) based on the maximization of mutual information (MMI) between features and class labels. We demonstrate the effectiveness of the proposed OLBP through several experiments of facial expression recognition. Experimental results show that the OLBP outperforms other features such as LBP, ULBP, and MCT in terms of minimizing the number of codes and reducing the classification error.