Intelligent facial expression recognition and classification using optimal deep transfer learning model

Facial expression is commonly utilized by humans to deliver their mood and emotional state to other people. Facial expression recognition (FER) becomes a hot research area in recent days, and it is a tedious process owing to the presence of high intra-class variation. The conventional methods for FEC are mainly based on handcrafted features with a classification model trained on image or video datasets. Since the facial datasets involve large variations in the images and comprise partial faces, it is needed to design automated FER models. The latest advancements in artificial intelligence (AI) and deep learning (DL) models find useful for better understanding of facial emotions related to face images. In this aspect, this paper presents an intelligent FER using optimal deep transfer learning (IFER-DTFL) model. The proposed IFER-DTFL technique aims to detect the face and identify the facial expressions automatically. The IFER-DTFL technique encompasses a three state process: face detection, feature extraction, and expression classification. In addition, a mask RCNN model is used for the detection of faces. Moreover, the Adam optimizer with Densely Connected Networks (DenseNet121) model is employed for feature extraction process. Furthermore, the weighted kernel extreme learning machine (WKELM) model is utilized to classify the facial expressions. A comprehensive set of simulations were carried out on benchmark dataset and the results are inspected under varying aspects. The experimental results pointed out the supremacy of the IFER-DTFL technique over the other recent techniques interms of several performance measures.