facial expression recognition based on image gradient and deep convolutional neural network

Facial expression recognition (fer) is one of the basic ways of interacting with machines and has been getting more attention in recent years. in this paper, a novel fer system based on a deep convolutional neural network (dcnn) is presented. motivated by the powerful ability of dcnn to learn features and image classification, the goal of this research is to design a compatible and discriminative input for pre-trained alexnet-dcnn. the proposed method consists of 4 steps: first, extracting three channels of the image including the original gray-level image, in addition to horizontal and vertical gradients of the image similar to the red, green, and blue color channels of an rgb image as the dcnn input. second, data augmentation including scale, rotation, width shift, height shift, zoom, horizontal flip, and vertical flip of the images are prepared in addition to the original images for training the dcnn. then, the alexnet-dcnn model is applied to learn high-level features corresponding to different emotion classes. finally, transfer learning is implemented on the proposed model and the presented model is fine-tuned on target datasets. the average recognition accuracy of 92.41% and 93.66% were achieved for jafee and ck+ datasets, respectively. experimental results on two benchmark emotional datasets show promising performance of the proposed model that can improve the performance of current fer systems.