Improved deep generative adversarial network with illuminant invariant local binary pattern features for facial expression recognition.

Facial Expression Recognition (FER) is one of the prevailing as well as demanding tasks in social communication. In general, face expressions are usual and straight ways for individuals to converse their intentions as well as emotions. This paper proposes the Taylor-Chicken Swarm Optimization-based Deep Generative Adversarial Network (Taylor-CSO-based Deep GAN) for FER. Here, the facial expressions are predicted through a series of steps, such as video frame extraction, pre-processing, face detection, feature extraction, as well as FER. The major contribution of the proposed work lies in the last step, where Taylor-CSO based Deep GAN is employed for recognizing facial expressions. Initially, video frames are extracted from the input video as well as pre-processing is done to extract the Region of Interest (RoI). Then, the Viola Jones algorithm is employed to detect the face images, and Illuminant Invariant Local Binary Pattern (IILBP) features are extracted. Finally, the FER is performed by the proposed model. The performance of the developed model is analyzed using the CK+ dataset, RAVDESS dataset, and SAVEE dataset. Also, the developed model's performance is evaluated with conventional FER methods. The performance analysis exhibits that the adopted model is precise and valuable. [ABSTRACT FROM AUTHOR]