Transfer Learning Based Face Emotion Recognition Using Meshed Faces and Oval Cropping: A Novel Approach

Abstract

The potential applications of emotion recognition from facial expressions have generated considerable interest across multiple domains, encompassing areas such as human-computer interaction, camera and mental health analysis. In this article, a novel approach has been proposed for face emotion recognition (FER) using several data preprocessing and Feature extraction steps such as Face Mesh, data augmentation and oval cropping of the faces. A transfer learning using VGG19 architecture and a Deep Convolution Neural Network (DCNN) have been proposed. We demonstrate the effectiveness of the proposed approach through extensive experiments on the Cohn-Kanade+ (CK+) dataset, comparing it with existing state-of-the-art methods. An accuracy of 99.79% was found using the VGG19. Finally, a set of images collected from an AI tool that generates images based on textual description have been done and tested using our model. The results indicate that the solution achieves superior performance, offering a promising solution for accurate and real-time face emotion recognition.