🤖 AI Summary
This study addresses key challenges in face recognition—such as illumination variations, aging, pose differences, occlusion, and facial expressions—by systematically reviewing and comparing mainstream approaches, including Principal Component Analysis (PCA), Eigenfaces, Independent Component Analysis (ICA), Gabor wavelets, Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Hidden Markov Models (HMMs), Elastic Bunch Graph Matching, and 3D Morphable Models. Through comprehensive experimental evaluations on standard benchmark datasets—including JAFEE, FEI, Yale, LFW, AT&T, and AR—the work analyzes the performance and applicability of each method under diverse and complex conditions. The resulting insights offer a well-structured, empirically grounded reference framework to guide future research in robust face recognition.
📝 Abstract
As multimedia content is quickly growing, the field of facial recognition has become one of the major research fields, particularly in the recent years. The most problematic area to researchers in image processing and computer vision is the human face which is a complex object with myriads of distinctive features that can be used to identify the face. The survey of this survey is particularly focused on most challenging facial characteristics, including differences in the light, ageing, variation in poses, partial occlusion, and facial expression and presents methodological solutions. The factors, therefore, are inevitable in the creation of effective facial recognition mechanisms used on facial images. This paper reviews the most sophisticated methods of facial detection which are Hidden Markov Models, Principal Component Analysis (PCA), Elastic Cluster Plot Matching, Support Vector Machine (SVM), Gabor Waves, Artificial Neural Networks (ANN), Eigenfaces, Independent Component Analysis (ICA), and 3D Morphable Model. Alongside the works mentioned above, we have also analyzed the images of a number of facial databases, namely JAFEE, FEI, Yale, LFW, AT&T (then called ORL), and AR (created by Martinez and Benavente), to analyze the results. However, this survey is aimed at giving a thorough literature review of face recognition, and its applications, and some experimental results are provided at the end after a detailed discussion.