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.
Abstract (translated)
随着多媒体内容的迅速增长，面部识别领域已成为近年来研究的重点之一。对于图像处理和计算机视觉的研究人员而言，最具挑战性的问题是人类脸部这个复杂对象，它包含众多独特的特征可以用来识别人脸。本文综述特别关注最具有挑战性的面部特性，包括光照变化、老化过程、姿势的变化、部分遮挡以及表情的影响，并提供了方法论上的解决方案。这些因素在创造有效的面部识别机制时不可避免。 本文回顾了最先进的面部检测方法，其中包括隐马尔可夫模型（Hidden Markov Models）、主成分分析法（PCA）、弹性集群图匹配（Elastic Cluster Plot Matching）、支持向量机（SVM）、Gabor滤波器、人工神经网络（ANN）、特征脸技术（Eigenfaces）、独立成分分析（ICA）以及3D形态学模型。除了上述工作之外，我们还对多个面部数据库的图像进行了分析，包括JAFEE、FEI、Yale、LFW、AT&T（当时称ORL），以及Martinez和Benavente创建的AR数据库，以进一步分析这些方法的效果。 然而，本文的主要目的是提供全面的脸部识别文献综述及其应用，并在详细讨论之后提供了部分实验结果。
URL
https://arxiv.org/abs/2601.06239
