Petersen Graph Based Binary Pattern for Person Independent Facial Expression Recognition

Facial expressions are utilized often in the day to day communication and are considered significant as they can mirror the internal emotional states of a person. Automatic Facial Expression Recognition (FER) systems aim at classifying the facial images into various expressions. To do this task accurately, better feature descriptors are to be developed to effectively capture the facial information. In this regard, novel local texture based feature extraction technique, Petersen Graph based Binary Pattern (PGBP), inspired by the Generalized Petersen Graph has been proposed. PGBP extracts three feature values in a 5 \(\,\times \,\) 5 overlapping neighborhood. The experiments have been performed on MUG, TFEID and KDEF datasets with respect to six expressions in person independent setup. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of recognition accuracy.