Face Emotion Recognition Using Combined Sift LBP and Local Phase Quantization Features
Recognizing facial expression has remained a challenging task in computer vision. Deriving effective facial expression recognition is an important step for successful human computer interaction systems. This paper describes a novel approach towards facial expression recognition task. Subsequently, we extract the features from Image using both Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) Then the results from both texture descriptors are tested on the Facial Expression Recognition. Occlusion is a big challenge for facial expression recognition (FER) in real world situations. The proposed here, two different methods of feature extraction for facial expression recognition from occluded images. The local binary pattern operator (LBP) and local Phase Quantization (LPQ) are used for feature extraction. Six basic facial expressions are considered. We consider four types of very frequently occurred occlusions in real world situations, the eyes/mouth and upper/lower face region occlusion. The experiments show that the proposed approach is robust to partial occlusion of the face.