Learning discriminative MspLBP features based on Ada-LDA for multi-class pattern classification

Abstract

This paper presents a novel approach for multi-class pattern classification - face detection and facial expression recognition, which is based on discriminative multi-scale and multi-position Local Binary Pattern (MspLBP) features selected by a boosting technique called the AdaBoost+LDA (Ada-LDA) method. From a large pool of MspLBP features within a face image, the most discriminative MspLBP features trained by two alternative LDA methods depending on the singularity of the within-class scatter matrix, are selected under the framework of AdaBoost. To verify the feasibility of our approach, we performed two extensive experiments on the famous face databases in terms of face detection and facial expression recognition. First, face detection, a typical example of two-class pattern classification, was carried out on the MIT-CBCL and MIT+CMU face test sets. Second, facial expression recognition, a typical problem of multi-class pattern classification, was performed on the JAFFE face database. Given the same number of features, the proposed face detector shows over 25% higher detection rate than the well-known Viola's detector at a given false positive rate of 10%. It can also provide real-time operation with over 10 frames per second rate. For facial expression recognition, our approach also shows a better performance over at least 21% recognition rates than other linear subspace-based methods such as PCA, DCV, and PCA+LDA. Our proposed approach provides a considerable performance improvement with only a small number of discriminative MspLBP features in the multi-class pattern classification problem.