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A Comprehensive Review Concerning the Involvement of Artificial Intelligence Techniques in Face Recognition System

A Comprehensive Review Concerning the Involvement of Artificial Intelligence Techniques in Face Recognition System
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Author(s): J. Vijaya (IIITNR, India), Soumya Chandrakar (IIITNR, India)and Pragya Shrivastava (IIITNR, India)
Copyright: 2026
Pages: 42
Source title: Practical Applications of Smart Human-Computer Interaction
Source Author(s)/Editor(s): Subrata Tikadar (Amity University, Kolkata, India), Joel José Puga Coelho Rodrigues (Federal University of Piauí, Brazil), Rabi Shaw (Dr. B.R. Ambedkar National Institute of Technology, India), Ahdi Hassan (Global Institute for Research Education and Scholarship, The Netherlands)and Bidyut Kumar Patra (Indian Institute of Technology, India)
DOI: 10.4018/979-8-3373-6385-1.ch003

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Abstract

This paper surveys the key methodologies used in the field of Face Recognition Systems, categorizing them into traditional techniques, machine learning approaches, deep learning models, and hybrid systems. Traditional methods such as Eigenfaces and Fisherfaces laid the foundation for face recognition by utilizing linear projection techniques. Machine learning methods like Support Vector Machines (SVM) and Random Forests have improved performance, particularly in scenarios with smaller datasets. The advent of deep learning has marked a significant turning point, with models like Convolutional Neural Networks (CNNs) and frameworks such as FaceNet and DeepFace achieving state-of-the-art accuracy and robustness across varying conditions, including illumination, occlusions, and facial expressions. Future directions discussed in this paper include advancements in explainable AI, fairness, federated learning, and scalable architectures to address these challenges while improving the robustness and scalability of face recognition systems.

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