Facial Expression Recognition Using Texture and Edge Descriptors

AbstractFacial expression recognition is one of the most important computer vision issues that has many applications. One of them is the Human computer interaction. In this paper, a method for facial expression recognition using texture and edge descriptors is proposed. Facial expression recognition generally consists of three steps: preprocessing, feature extraction and classification. In this paper, histogram Equalization has been used in the proposed method for pre-process the input images in which the face is present. In this paper, the focus is on the feature extraction and a combination of LDP۱ and HOG۲ descriptors has been used to improve the existing methods. After feature extraction, the support vector machine was used to classification the facial expression recognition. This article uses the JAFFE database. The database contains ۲۱۳ images of seven facial expressions (happy, sad, angry, fear, disgust, surprised and natural) taken from ۱۰ Japanese female models. The results showed that the proposed method with ۹۹.۰۴% accuracy in the facial recognition test had a better performance than the methods of previous researchers.

Keywords:

Facial Expression Recognition , histogram equalization , texture and edge descriptors , LDP , HOG , Support vector machine