Can edges help convolution neural networks in emotion recognition? - Bhandari, Arkaprabha; Pal, Nikhil R. (Neurocomputing (Amsterdam), 2021)
Facial emotion recognition has gained importance for its applications in diverse areas. Facial expressions of a subject, when experiencing the same emotion, have wider variations. On the other hand, different subjects experiencing the same emotion may exhibit different facial features. All these make facial emotion recognition challenging. The ability of convolutional neural network (CNN) has been exploited to analyze visual imagery for different applications. It has also been used in developing automatic facial emotion recognition systems. Our objective in this study is to check if an explicit use of edges can help emotion recognition from images using CNN. Edges in an image represent discriminatory information and hence their explicit use is likely to help the training of CNNs and improve emotion recognition. Keeping this in mind we propose a two-tower CNN architecture to classify images into seven basic classes of emotion including the neutral expression. The proposed CNN has an additional tower, called edge-tower, which is simpler in architecture compared to the other tower and it uses edge images as inputs. Our experiments on two benchmark datasets demonstrate that the explicit use of edge information improves the classifier performance.