人脸检测研究综述

人脸检测问题最初作为自动人脸识别系统的定位环节被提出，近年来由于其在安全访问控制，视觉监测、基于内容和检索和新一代人机界面等领域的应用价值，开始作为一个独立的课题受到研究者的普遍重视。该文从人脸检测问题的分类、人脸模式的分析、特征提取与特征综合、性能评价等角度，系统地整理分析了人脸检测问题的研究文献，将人脸检测方法主要划分为基于知识的人脸验证方法和基于统计的学习方法，指出统计学习方法优于启发式验证方法。

A Survey of Human Face Detection

This paper presents a survey on the state of the art of face detection research based on systematic analysis of related papers. Firstly face detection problem is divided into several classes according to the type of input images, background complexity, pose variance, application domain etc., and then face pattern is analyzed based on various features and their possible fusion method for the purpose of face detection. The literature is reviewed in two parts: feature extraction and feature fusion for face detection. Feature extraction includes skin color segmentation and various gray level features such as the outline of face, gray level distribution, organic feature, symmetry, template etc. Feature fusion methods include knowledge based heuristic face verification, statistical learning approaches (Eigenface, Clustering, ANN, SVM, HMM, EM probabilistic model). Performance comparison of some well known methods is given on MIT+CMU test set. In conclusion, statistical learning methods are superior to those knowledge based methods, and in all those learning based methods, the key problem is the training complexity, even by bootstrap method it remains a great challenge due to the diversity of non face samples compared with face samples. We suggest a subspace method for downsizing the training space by designing a filter (such as template matching filter) that excludes most of non face candidates and then training in the downsized subspace. It is pointed out that statistical learning methods depend on the accordance of sample patterns (syntactic information), which cannot take into considerations of much important semantic information. This differs much from human beings in face cognition. There is a limit for statistical only approaches and the help of knowledge based methods is needed.