Support Vector Slant Binary Tree Architecture for Facial Stress Recognition Based on Gabor and HOG Feature

The facial stress recognition with histogram information is discussed in this paper. The patterns of facial stress are recognized into three stages, these include a registered image, feature extraction, and classification. The registered image process takes three important parts of the face (the pair of eyes, the nose, and the mouth). While the histogram method such as Gabor filter and HOG feature is used as the feature extraction. Furthermore, the proposed method is to combine SVM and Slant Binary Tree algorithm. Each part of face processed on a different pipeline. Based on the experiment result, the proposed system has accuracy about 86.7{\% and it is outperforming the previous methods. The results of the tree structure show that the nose is a part of the face that most indicates stress.