An End-to-End Deep Model With Discriminative Facial Features for Facial Expression Recognition

Jun Liu, Hongxia Wang, Yanjun Feng
2021 IEEE Access  
Due to the complex challenges of the environment and emotion expressions, most facial expression recognition systems cannot achieve a high recognition rate. More discriminative features can describe facial expressions more accurately, so facial feature extraction is the key technology for facial expression recognition. In this article, an effective end-to-end deep model is proposed to improve the accuracy of face recognition. Considering the importance of data pre-processing (very few studies
more » ... ve focused on this process), first, a data enhancement method is proposed to locate the range of the face target and enhance the image contrast. Next, to obtain further discriminative features, a hybrid feature representation method is proposed, in which four typical feature extraction method are combined. After that, an effective deep model is designed to train and test the samples which can obtain the optimal parameters with less computation cost. Ablation study results show that the proposed hybrid feature representation method can help improve recognition accuracy. Finally, to comprehensively evaluate the performance of the proposed model, a series of experiments are conducted on three benchmark datasets. The recognition rate is achieved 94.5%, 98.6%, and 97.2% for FER2013, AR dataset, and CK+ dataset, respectively. INDEX TERMS Face recognition, feature extraction, data enhancement, CNN. 12158 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 9, 2021
doi:10.1109/access.2021.3051403 fatcat:btlf4o2qpvdufoyje4dwc72upi