Open AccessFace Recognition System for Control Access to Restrictive Domain
Author(s) -
Donald Mouafo,
Ulrich Biaou
Publication year - 2022
Publication title -
international journal of safety and security engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.202
H-Index - 10
eISSN - 2041-904X
pISSN - 2041-9031
DOI - 10.18280/ijsse.120214
Subject(s) - facial recognition system , artificial intelligence , computer science , feature extraction , pattern recognition (psychology) , three dimensional face recognition , support vector machine , face (sociological concept) , face detection , feature (linguistics) , process (computing) , machine learning , social science , linguistics , philosophy , sociology , operating system
Recent progress in computer vision applied to facial analysis has led to state-of-the-art face detection and facial feature extraction models. A cautious implementation of these models into face recognition pipelines can enable achieving superior performances and popularized daily applications of face recognition in a variety of domains. However, modern face recognition system is a multi-steps process including face detection, feature extraction and classification model. Developing a high-performance face recognition application generalizing on local data set remains challenging. In this paper, we present Deep learning based face recognition system employing MTCNN for face detection and FaceNet for feature extraction. We compare KNN and SVM classification models trained on the facial features extracted from prepared labeled faces. Both models demonstrated almost 100% accuracy on static test faces. Moreover, as face pose get more pronounced, far above 30°, both SVM and KNN models demonstrate efficient recognition rate of 95.95% and 96.67% respectively. Real-time evaluation shows less than 1% deviation from the static performances with both classifiers on less 30° tilted images.