Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

Abstract

Although lots of research has been done in recognizing facial expressions, there is still a need to increase the accuracy of facial expression recognition, particularly under uncontrolled situations. The use of Local Directional Patterns (LDP), which has good characteristics for emotion detection has yielded encouraging results. An innovative end-to-end learnable High Response-based Local Directional Pattern (HR-LDP) network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work. By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions, this network considerably minimizes the number of network parameters. The cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection algorithms. On seven well-known databases, including JAFFE, CK+, MMI, SFEW, OULU-CASIA and MUG, the recognition rates for seven-class facial expression recognition are 99.36%, 99.2%, 97.8%, 60.4%, 91.1% and 90.1%, respectively. The results demonstrate the advantage of the proposed work over cutting-edge techniques.