Key facial points recognition using ResNet

Abstract

Key facial points recognition is a major research topic in the domain of image processing. In this process various important facial points such as nose tip, eye center etc are detected using machine learning and AI. Key facial points recognition can help for the face emotion recognition. Although convolutional neural networks (CNN) can be used for face recognition but the CNN suffers from the vanishing gradient problem. While training the CNN model using back propagation algorithm the vanishing gradient problem occurs. Therefore the models stops training as the gradient become vanishingly small. To solve this problem a ResNet model has been implemented in this paper. The ResNet model solves the vanishing gradient problem with the help of an additional skip connection. And therefore a deep neural network can be constructed using the ResNet. The proposed ResNet model identifies the key facial points with high accuracy. After identifying the key facial points one can easily identify the emotion class of the face image. The detected key facial points in this experiment are left eye center, right eye center, left eye inner corner, left eye outer corner, right eye inner corner, nose tip, mouth left corner, mouth right corner, mouth right corner, mouth center top lip, mouth center bottom lip. After training the proposed ResNet model for 100 epochs it successfully detected the key facial points with 77.70 percent accuracy.