Single and Multiface Detection and Recognition System

Face detection has drawn the interest of numerous research groups because to its vast application in various domains such as surveillance and security systems, as human-computer interaction, and many more. Face identification is the important phase involving several factors such as lighting, facial...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-08, Vol.2312 (1), p.12036
Main Authors: Ariff, F N M, Jaafar, H, Jusoh, S N H, Haris, N A F
Format: Article
Language:English
Subjects:
Face recognition
Feature extraction
Image classification
Object recognition
Physics
Principal components analysis
Security systems
Support vector machines
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Summary:Face detection has drawn the interest of numerous research groups because to its vast application in various domains such as surveillance and security systems, as human-computer interaction, and many more. Face identification is the important phase involving several factors such as lighting, facial expression, and ageing effects. It’s more tough as detection takes a lot of time to detect and distinguish a single face at a time. Moreover, most of the existing technology cannot accurately detect many faces simultaneously. This study therefore presents a system that can recognize and identify multiple face image simultaneously with various expressions. Face-recognition procedure consists of data gathering, face detection, extraction, and classification feature. The face dataset is obtained from 10 participants with varied backgrounds and expressions. Subsequently, the viola-jones technique together with threshold technique is utilized in face detection to detect face presents while removing the unnecessary background to reduce face recognition time processing further. The Principal Component Analysis (PCA) is then employed to extract features while maintaining as much information as possible from enormous image data set. After formulating each face’s representation, the classification process is considered to recognize the identities of users’ faces. Here, a non-parametric classifier i.e. Support Vector Machine (SVM) is applied in this process. Conclusively, the system is able to detect around 90 percent multi-face user in different conditions.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2312/1/012036