Visible to the public Real-Time Facial Emotion Recognition System With Improved Preprocessing and Feature Extraction

TitleReal-Time Facial Emotion Recognition System With Improved Preprocessing and Feature Extraction
Publication TypeConference Paper
Year of Publication2020
AuthorsJohn, A., MC, A., Ajayan, A. S., Sanoop, S., Kumar, V. R.
Conference Name2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)
Date Publishedaug
KeywordsConferences, convolutional neural nets, convolutional neural network, convolutional neural network (CNN), emotion recognition, exact facial expression, face recognition, faces, facial emotion, facial expressions, Facial features, facial landmark, facial recognition, feature extraction, FER2013, gesture recognition, hand gestures, Human Behavior, human communication, human computer interaction, human emotion recognition, human-machine interaction domain, Interpersonal Communication, JAFFE, lighting, Metrics, pre-processing, pubcrawl, real-time facial emotion recognition system, Real-time Systems, resilience, Resiliency

Human emotion recognition plays a vital role in interpersonal communication and human-machine interaction domain. Emotions are expressed through speech, hand gestures and by the movements of other body parts and through facial expression. Facial emotions are one of the most important factors in human communication that help us to understand, what the other person is trying to communicate. People understand only one-third of the message verbally, and two-third of it is through non-verbal means. There are many face emotion recognition (FER) systems present right now, but in real-life scenarios, they do not perform efficiently. Though there are many which claim to be a near-perfect system and to achieve the results in favourable and optimal conditions. The wide variety of expressions shown by people and the diversity in facial features of different people will not aid in the process of coming up with a system that is definite in nature. Hence developing a reliable system without any flaws showed by the existing systems is a challenging task. This paper aims to build an enhanced system that can analyse the exact facial expression of a user at that particular time and generate the corresponding emotion. Datasets like JAFFE and FER2013 were used for performance analysis. Pre-processing methods like facial landmark and HOG were incorporated into a convolutional neural network (CNN), and this has achieved good accuracy when compared with the already existing models.

Citation Keyjohn_real-time_2020