Local triangular patterns: novel handcrafted feature descriptors for facial expression recognition

Abstract

Facial expressions are common across cultures and are used universally by humans to express their internal emotions, intentions and thoughts. The main task in facial expression recognition (FER) systems is to develop efficient feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting significant features, local triangular patterns (LTrP) named mini triangular pattern (mTP) and mega triangular pattern (MTP) have been proposed in order to minimise the feature vector length and to maximise the recognition accuracy. The proposed mTP method extracts two features in a 3 × 3 circular neighbourhood, whereas, MTP method extracts two features in a 5 × 5 circular neighbourhood. The proposed methods (mTP and MTP) are implemented on three benchmark FER datasets namely TFEID, MUG and KDEF. The experiments have been performed with respect to both six and seven expressions in person independent setup to simulate a real-world scenario. The experimental results demonstrated the efficiency of the proposed methods when compared to the standard existing methods.