Open AccessHuman Behavior Analysis through Facial Expression Recognition in Images using Deep Learning
Author(s) -
Chintan B Thacker*,
Ramji M. Makwana
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6379.129219
Subject(s) - facial expression , anger , expression (computer science) , computer science , task (project management) , artificial intelligence , deep learning , hatred , field (mathematics) , emotion classification , sentiment analysis , process (computing) , meaning (existential) , cognitive psychology , psychology , social psychology , mathematics , management , politics , political science , pure mathematics , law , economics , psychotherapist , programming language , operating system
Facial Expression Recognition is an important undertaking for the machinery to recognize different expressive alterations in individual. Emotions have a strong relationship with our behavior. Human emotions are discrete reactions to inside or outside occasions which have some importance meaning. Involuntary sentiment detection is a process to understand the individual’s expressive state to identify his intensions from facial expression which is also a noteworthy piece of non-verbal correspondence. There are seven essential emotions which incorporate Cheerful, Gloomy, Anger, Terror, Astonish, Hatred as well as Unbiased. In the present period of HumanComputer Interaction (HCI), making machines to analyze and recognize emotions is a difficult task. Recent FER systems are lacking of sufficient training data and other problems like illumination and head pose to identify emotions. Inside this article, we provide a comprehensive learning of Facial expression detection with Deep Learning methods which includes different Neural Network Algorithms used with different datasets and its efficiency result. Also we will provide current challenges and current opportunities in this field to develop robust FER using Deep learning.