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EmotionFusion: A unified ensemble R-CNN approach for advanced facial emotion analysis

计算机科学 厌恶 人工智能 藐视 面部表情 卷积神经网络 情绪分类 深度学习 模式识别(心理学) 特征(语言学) 惊喜 特征提取 支持向量机 语音识别 愤怒 心理学 社会心理学 语言学 哲学 神经科学 精神科
作者
A. Umamageswari,S. Deepa,A Bhagyalakshmi,Arash Sangari,Kiran Raja
出处
期刊:Journal of Intelligent and Fuzzy Systems [IOS Press]
卷期号:45 (6): 10141-10155
标识
DOI:10.3233/jifs-233842
摘要

To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network’s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry.

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