Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter

Abstract

Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions and to recognize human emotions. Recently, deep learning models are utilized to improve the performance of facial emotion recognition. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, an efficient deep learning-based facial emotion recognition model is proposed. Initially, contrast-limited adaptive histogram equalization (CLAHE) is applied to improve the visibility of input images. Thereafter, a modified joint trilateral filter is applied to the obtained enhanced images to remove the impact of impulsive noise. Finally, an efficient deep convolutional neural network is designed. Adam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are conducted by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models