JACIII Vol.24 No.6 pp. 792-801
doi: 10.20965/jaciii.2020.p0792


Two-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition

Chang Liu, Kaoru Hirota, Bo Wang, Yaping Dai, and Zhiyang Jia

School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 10081, China

Corresponding author

October 13, 2020
October 19, 2020
November 20, 2020
facial expression recognition, convolutional neural network, local binary pattern, texture feature

An emotion recognition framework based on a two-channel convolutional neural network (CNN) is proposed to detect the affective state of humans through facial expressions. The framework consists of three parts, i.e., the frontal face detection module, the feature extraction module, and the classification module. The feature extraction module contains two channels: one is for raw face images and the other is for texture feature images. The local binary pattern (LBP) images are utilized for texture feature extraction to enrich facial features and improve the network performance. The attention mechanism is adopted in both CNN feature extraction channels to highlight the features that are related to facial expressions. Moreover, arcface loss function is integrated into the proposed network to increase the inter-class distance and decrease the inner-class distance of facial features. The experiments conducted on the two public databases, FER2013 and CK+, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 72.56% and 94.24%, respectively. The improvement in emotion recognition accuracy makes our approach applicable to service robots.

Cite this article as:
Chang Liu, Kaoru Hirota, Bo Wang, Yaping Dai, and Zhiyang Jia, “Two-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 792-801, 2020.
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Last updated on Mar. 05, 2021