Robust Facial Expression Classification Using Shape and Appearance Features

TL;DR

This paper introduces a hybrid feature extraction method combining shape and appearance features from active facial patches for robust and efficient facial expression classification, achieving promising accuracy on public datasets.

Contribution

It proposes a novel hybrid feature extraction approach from active facial patches, reducing computational cost and overfitting in facial expression recognition.

Findings

High accuracy in recognizing expressions on public datasets

Effective reduction of computational cost and overfitting

Successful integration of shape and appearance features

Abstract

Facial expression recognition has many potential applications which has attracted the attention of researchers in the last decade. Feature extraction is one important step in expression analysis which contributes toward fast and accurate expression recognition. This paper represents an approach of combining the shape and appearance features to form a hybrid feature vector. We have extracted Pyramid of Histogram of Gradients (PHOG) as shape descriptors and Local Binary Patterns (LBP) as appearance features. The proposed framework involves a novel approach of extracting hybrid features from active facial patches. The active facial patches are located on the face regions which undergo a major change during different expressions. After detection of facial landmarks, the active patches are localized and hybrid features are calculated from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost and prevents the over-fitting of the features for classification. By using linear discriminant analysis, the dimensionality of the feature is reduced which is further classified by using the support vector machine (SVM). The experimental results on two publicly available databases show promising accuracy in recognizing all expression classes.