Ensemble of Multi Features for Facial Expression Recognition using Deep Learning Techniques

Abstract

newline As we move towards a digital world, Human-Computer Interaction becomes very important. Facial Expressions are the key features of non-verbal communication and they play an essential role in human-computer interaction. Facial Expressions play a crucial role in social interactions and commonly used in the behavioural interpretation of emotions. It becomes easy to understand anyone s emotional state and intentions based on the shown facial expression. Over the last few years, facial expression recognition has attracted researchers in psychology, computer science, security and medicine-related fields. These fields have an extensive range of applications like in surveillance cameras to identify suspicious person, patient s painful situation at hospital, online meeting or in E-learning system, music player play songs based on person s mood, driver s tiredness from his expression while driving, robotics, behavioural science, etc. based on facial expressions. Although human beings can identify the facial expressions correctly and effortlessly, still reliable automatic facial expression recognition by machines is a challenge. Facial expression recognition system consists of different stages like Face Detection, Feature Extraction and Emotion Classification. There are seven universally defined facial expressions: Angry, Disgust, Fear, Happy, Neutral, Sad and Surprise. Facial expression recognition using the Convolutional Neural Network has been actively researched in the last decade due to its high number of applications in the human-computer interaction domain. As Convolutional Neural Networks have an exceptional capability to learn, they outperform well on features using its different pre-trained architectures. Existing state-of-the-art models have achieved good recognition accuracy on laboratory trained facial expression datasets; however, they struggle to achieve good accuracy for the real-time facial expression datasets trained in an uncontrolled environment. Images captured in an uncontrolled setting o