HUMAN-COMPUTER INTERACTION FOR DETECTING THE AFFECTIVE STATE USING FACIAL EXPRESSION
Received: 21.08.2021; Revised: 30.09.2021, Accepted: 26.10.2021, Published Online: 10.11.2021
Ahmed Kareem
School of Information Engineering, Zhengzhou University,Zhengzhou ,450001, China.
Yongcai Tao
School of Information Engineering, Zhengzhou University,Zhengzhou ,450001, China.
Lin Wei
School of Software, Zhengzhou University, Zhengzhou,450001, China.
Bei Yang
School of Information Engineering, Zhengzhou University,Zhengzhou ,450001, China.
Yufei Gao
School of Software, Zhengzhou University, Zhengzhou,450001, China, yfgao@zzu.edu.cn, yfgao@mail.bnu.edu.cn
Lei Shi
School of Software, Zhengzhou University, Zhengzhou,450001, China.
Abstract
Human-Computer communication would be upgraded in a cordial and non-meddlesome manner if computers could comprehend and react to clients’ non-verbal communication similarly. Past investigations have shown that there is a nearby and stable connection between an individual’s looks and feelings for the most part. Emotional state acknowledgment is commonly founded on inactive upgrades, for example, watching video cuts, which doesn’t reflect veritable association. This paper presents an examination on emotional state acknowledgment utilizing dynamic improvements, for example looks of clients when they endeavor electronic assignments, especially across commonplace use of computer frameworks according to the viewpoint of programmatic experience, a structure joining face effective recognition (FER) calculation with various stages is proposed in this work. An information assortment explore is introduced for obtaining information from ordinary clients while they interface with programming, endeavoring to finish a bunch of predefined jobs. In addition, a cutting-edge AI solution for look-based full of feeling state recognition is introduced, which employs a Euclidean distance-based component portrayal, as well as a modified encoding for clients’ self-detailed emotional states. The proposed research work’s usefulness and robustness are demonstrated by the test findings.
Keywords- Facial expressions, Human-computer interaction, Euclidean distance, hierarchical machine learning