Open AccessMultilayer Convolution Sparse Coding for Expression Recognition
Author(s) -
Shuda Chen,
Yan Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012086
Subject(s) - computer science , convolutional neural network , facial expression recognition , artificial intelligence , pattern recognition (psychology) , interpretability , neural coding , facial expression , facial recognition system , feature extraction , coding (social sciences) , convolution (computer science) , feature (linguistics) , speech recognition , artificial neural network , mathematics , linguistics , statistics , philosophy
Facial expression recognition is widely used in various research fields. For facial expression recognition problems, deep neural network methods have a complex structure and poor interpretability, while traditional machine learning methods have less plentiful diverse features and low recognition rates. Therefore, a new Multilayer Convolution Sparse Coding (MCSC) method is proposed for facial expression recognition. The MCSC method deeply extracts the salient features of the human face through a convolutional neural network. Furthermore, it uses a multilayer sparse coding to learn layer by layer to recognize different facial expression features based on sparse coding, which improves the recognition accuracy of facial expressions. Finally, the MCSC method was validated on three public facial expression datasets, i.e. JAFFE, CK +, and Fer2013. We also compared and analyzed 5 feature extraction approaches. The results show that MCSC has the best facial expression recognition performance in the comparison algorithm. Its accuracies of the three data sets reach to 90.8%, 98.2%, and 72.4%, respectively.