Face emotion recognition using vertically deflected and horizontally deflected face images

Abstract

Face Emotion Recognition has been a one of the common fields of work ever since Deep Leaning technology has been introduced. Although a lot of techniques have acquired a high performance, yet this technology has not been used for real-life application. This is caused by the lack of robustness in Face Emotion Recognition. Most of the works on Face Emotion Recognition focuses to extract the facial features and the emotion features mostly from profile face images. However, it is important to train the models to be able to detect faces and recognize emotions from a side angle or a tilted as well in order to make sure the model is real-life applicable. As most of the probable uses of Face Emotion Recognition requires that the model is capable of recognizing an emotion from various angles whether it is vertically deflected or horizontally. Therefore, we have trained two CNN models MobileNetV2 and VGG16 on a dataset that contains both profile faces and faces of vertically deflected images and horizontally deflected angles. After detecting the face location in an image using DNN from face_recognition library, using the transfer learning approach, we achieved 87% and 60% training accuracy from VGG16 and MobileNetV2 respectively. Therefore, the models can be used to detect face emotions from both profile images and side faced images. With such a robustness, we are one step further to optimizing a Face Emotion Recognition technology that could be used various real-life events to develop the quality of services, feedbacks and analyzing demands in different industries.