6533b83afe1ef96bd12a78f9

RESEARCH PRODUCT

Deep Residual Neural Network for Child’s Spontaneous Facial Expressions Recognition

subject

description

Early identification of deficits in emotion recognition and expression skills may prevent low social functioning in adulthood. Deficits in young children’s ability to recognize facial expressions can lead to impairments in social functioning. Kids may need extra help learning to read facial expressions. Most of the earlier efforts consider the problem of emotion recognition in adults; however, ignore the child’s emotions, especially in an unconstrained environment. In this paper, we present progressive light residual learning to classify spontaneous emotion recognition in children. Unlike earlier residual neural network, we reduce the skip connection at the earlier part of the network and increase gradually as the network go deeper. The progressive light residual network can explore more feature space due to limiting the skip connection locally, which makes the network more vulnerable to perturbations which help to deal with overfitting problem for smaller data. Experimental results on benchmark children emotions dataset show that the proposed approach showed a considerable gain in performance compared to the state of the art methods.