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.

翻译：随着多媒体内容的快速增长，人脸识别领域已成为主要研究方向之一，近年来尤其如此。对于图像处理和计算机视觉领域的研究者而言，人脸是最具挑战性的研究对象之一——作为具有无数可用于身份识别的独特特征的复杂对象。本综述特别关注最具挑战性的人脸特征，包括光照差异、年龄变化、姿态变化、局部遮挡和面部表情，并提出了相应的解决方案。这些因素对于构建基于人脸图像的有效识别机制至关重要。本文回顾了最先进的人脸检测方法，包括隐马尔可夫模型、主成分分析（PCA）、弹性簇图匹配、支持向量机（SVM）、Gabor小波、人工神经网络（ANN）、特征脸、独立成分分析（ICA）和三维可变形模型。除上述方法外，我们还分析了一系列人脸数据库的图像，包括JAFEE、FEI、Yale、LFW、AT&T（原称ORL）以及Martinez与Benavente创建的AR数据库，以评估各方法性能。本综述旨在提供全面的人脸识别文献综述及其应用研究，并在详细讨论后附上部分实验结果。