Feature Fusion of Gradient Direction and LBP for Facial Expression Recognition

Feature extraction is an important step in facial expression recognition. A novel method is proposed based on feature fusion which combines gradient direction and LBP features. Firstly, eyes are located through the integration projection method. And the operation of image rotating, cropping and normalizing is conducted based on eyes' position. Secondly, the image is partitioned into nine non-overlapping regions with different weight, then the gradient direction and LBP features are extracted and fused. The fused features generated from each of the regions are concatenated to form the feature vector which represents the facial expression. Finally, K-nearest neighbor algorithm is performed for classification. Experiments on JAFFE and Cohn-Kanade facial expression databases show that the proposed method achieves better performance for facial expression recognition.