Local directional pattern variance (LDPv)‎ : robust feature descriptor for facial expression recognition

Abstract EN

Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in the applications of human-computer interactions.

The vital component of any successful expression recognition system is an effective facial representation from face images.

In this paper, we have derived an appearance-based feature descriptor, the Local Directional Pattern Variance (LDP v), which characterizes both the texture and contrast information of facial components.

The LDP v descriptor is a collection of Local Directional Pattern (LDP) codes weighted by their corresponding variances.

The feature dimension is then reduced by extracting the most discriminative elements of the representation with Principal Component Analysis (PCA).

The recognition performance based on our LDP v descriptor has been evaluated using Cohn-Kanade expression database with a Support Vector Machine (SVM) classifier.

The discriminative strength of LDP v representation is also assessed over a useful range of low resolution images.

Experimental results with prototypic expressions show that the LDP v descriptor has achieved a higher recognition rate, as compared to other existing appearance-based feature descriptors.