EFNet: An Effective Facial Expression Recognition Network for Infants

Facial expression plays a crucial role during interactions with people. Previous studies on facial expression recognition (FER) have mainly focused on adults, while there are few studies on FER for infants. Due to the apparent differences in facial proportions and facial contours between infants and adults, the FER studies for infants could not be conducted on existing expression datasets. In order to study infant facial expressions in-depth, we create the infant facial expression recognition (IFER) dataset by collecting 10,240 infant images. Since infants' faces have smooth facial lines and weak sharpness, the inter-class similarity of facial expressions is higher than adults, and the existing networks for facial expression recognition lack attention to inter-class similarity. To address the above problems, we propose an effective infant facial expression recognition network named EFNet. In the first stage, the convolutional neural network (CNN) branch and the self-attention branch extract the overall features of infants' faces. In the second stage, we propose the self-adaptive attentional centre loss (SACL). The SACL uses the extracted feature maps as contexts to estimate the weights by an attention mechanism and then applies the attentional weights to guide the centre loss. Overall, the SACL facilitates inter-class separateness and intra-class compressiveness of related information in an embedding space. The state-of-the-art results on the IFER dataset confirm the remarkable effectiveness of the EFNet.