Facial Expression Recognition Adopting Combined Geometric and Texture-Based Features

Abstract

Abstract In recent facial expression recognition competitions, top approaches were using either geometric relationships that best captured facial dynamics or an accurate registration technique to develop texture features. These two methods capture two different types of facial information that is similar to how the human visual system divides information when perceiving faces. This paper discusses a framework of a fully automated comprehensive facial expression detection and classification. We study the capture of facial expressions through geometric and texture-based features, and demonstrate that a simple concatenation of these features can lead to significant improvement in facial expression classification. Each type of expression has individual differences in the commonality of facial expression features due to differences in appearance and other factors. The geometric feature tends to emphasize the facial parts that are changed from the neutral and peak expressions, which can represent the common features of the expression, thus reducing the influence of the difference in appearance and effectively eliminating the individual differences. Meanwhile, the consolidation of gradient-level normalized cross correlation and Gabor wavelet is utilized to present the texture features. We perform experiments using the well-known extended Cohn-Kanade (CK+) database, compared to the other state of the art algorithms, the proposed method achieved provide better performance with an average accuracy of 95.3%.KeywordsFacial expression recognitionGeometry featureTexture feature