DEVELOPMENT OF A MODEL FOR IMPROVING FACE-ID TECHNOLOGY IN ENSURING INFORMATION SECURITY ON E-PLATFORMS
DOI:
https://doi.org/10.5281/zenodo.20496996Abstract
This article analyzes modern methods and algorithms for the automatic recognition of human
emotions through facial expressions. The study examines the processes of facial expression identification
based on the Facial Action Coding System (FACS). The possibilities of detecting emotional states, including
excitement, confusion, concentration, and nervousness, through the identification of facial landmark points and
the distances between them are discussed. Furthermore, the effectiveness of using the Tree-Augmented Naive
Bayes (TAN) classifier and the CK+ database in facial expression analysis is investigated.
The article also proposes a semantic model of an automated facial expression recognition system and
substantiates its significance in the educational process, particularly in improving authentication and monitoring
systems within electronic learning platforms. The proposed system enables continuous monitoring of students’
emotional states, enhances security during examination processes, and automates personal identification
procedures.
Keywords
Facial expression recognition, emotion recognition, Facial Action Coding System (FACS), Action Units (AU), facial landmarks, biometric identification, Tree-Augmented Naive Bayes (TAN) classifier, CK+ database, artificial intelligence, computer vision, human-computer interaction, e-learning, authentication, facial movement analysis, emotional state monitoring.References
Cohen I, Sebe N, Garg A, Lew M S and Huang T S 2003 Facial Expression Recognition From Video Sequences
Computer Vision and Image Understanding 91(1-2) 160-187p
Valenti R, Sebe N and Gevers T 2007 Facial Expression Recognition: A Fully Integrated Approach Image Analysis and
Processing Workshops IEEE DOI 10.1109/ICIAPW.2007.25
Paleari M, Huet B and Chellali R 2010 Towards Multimodal Emotion Recognition: New Approach ACM International
Conference on Image and Video Retrieval
Paleari M, Chellali R and Hue B 2010 Features for Multimodal Emotion Recognition:An Extensive Study Cybernetics
and Intelligent Systems (CIS) IEEE DOI:10.1109/ICCIS.2010.5518574
Hsu F S, Lin W Y and Tsai T -W 2014 Facial expression recognition using bag of Distances Multimedia Tools and
Applications73(1) 309–326
Saneiro M, Santos O C, Salmeron-Majadas S and Boticario J G 2014 TowardsEmotion Detection in Educational
Scenarios from Facial Expressions and Body Movements through Multimodal Approaches The Scientific World Journal
DOI: 10.1155/2014/484873
Moridis C N and Economides E A 2009 Mood Recognition during OnlineSelf Assessment Tests IEEE Transactions on
Learning Technologies 2(1) 50-61
Lucey P, Cohn J F, Kanade T,Saragih J and Ambadar Z 2010 Extended Cohn-Kanade Dataset (CK+): A complete
dataset for action unit and emotion specified expression Computer Vision and Pattern Recognition Workshops
(CVPRW) DOI: 10.1109/CVPRW.2010.5543262
Tomasi C and Kanade T 1991 Detection and Tracking of Point Features Technical Report CMU-CS-91-132
Akputu K O, Seng K P and Lee Y L 2013 Facial Emotion Recognition for Intelligent Tutoring Environment 2nd
International Conference on Machine Learning and Computer Science (IMLCS)
Downloads
Published
How to Cite
Issue
Section
