Multi-Feature Fusion based Facial Expression Classification using DLBP and DCT
Ch. VenkataRamiReddy, Venkata Krishna Kishore Kolli, Debnath Bhattacharyya, Tai-hoon Kim · International Journal of Software Engineering and Its Applications · 2014
Classification of facial expressions is a great challenge in the area of computer vision. Using single feature models, the percentage of recognition is considerably low even in controlled conditions of capturing. To improve the accuracy of the facial emotion recognition and classification, a new method is proposed in this paper based on the fusion of features extracted from different techniques. Local features are extracted using Directional Local Binary Patterns (DLBP) and global features are extracted using Discrete Cosine Transform (DCT) from facial expression images. Principal Components Analysis (PCA) is used to reduce the dimensions of extracted features. Weighted summation and PCA fusion methods are used to fuse the local and global features extracted from facial images. RBF neural network is used as classifier for classification of facial images into six emotions (surprise, fear, sad, joy, anger and disgust). Cohn-Kanade database is used to evaluate the proposed method. The results of proposed algorithm yield better recognition rate of 97% in comparison with existing methods.