Improved Xception with Local Dense Connections and Transition Layer for Facial Expression Recognition
Year: 2024
Type: conference-paper
Abstract: Traditional deep convolutional neural networks are used for facial expression recognition, which makes the number of neurons and parameters huge, wastes computing resources, and even causes problems such as overfitting and network degradation. Meanwhile, single-scale expression features cannot describe rich facial expression information and are not evenly distributed on face images. We proposed an improved Xception method based on the original Xception network, it retains the depth-separable convolution and residual structure, adds multi-scale convolution and local dense connection. Our proposed method achieved an accuracy rate of 89.54% on the FER2013 dataset, strengthened feature reuse, and enhanced generalization ability.
Institutions: Xinyu University, Universiti Teknologi MARA
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4407030749