Abstract: Based on video surveillance, this paper designs a kind of face detection, tracking recognition system, the function of which is detecting and tracking face image real-timely in video, at the same time for recognizing for the identity. The cascade classifier that Gentle AdaBoost algorithm constructs has a problem of low efficiency. To deal with that it proposes a cascade classifier in progressive complexity. In view of the highest weight particles of the traditional particle filter may not be accurate, the weight equal value of particle filter is proposed. Aiming at the sample recession problem of traditional particle filter, it puts forward a strategy of tracking correction that combines face detection and face tracking algorithm. The method based on Gabor transform and the HMM is used to identify the faces which are detected and tracked. The experiment results show that in the video, the system is capable of detecting the faces accurately and tracking them real-timely, it can also realize the fast recognition. It is an effective method that can be applied to the video surveillance system.

Key words: progressive structure, weight equal value, tracking correction, Gabor feature, Hidden Markov Model（HMM）

摘要： 设计了一种基于视频监控的人脸检测跟踪识别系统，该系统的功能是检测并实时跟踪视频中的人脸图像，同时进行身份识别。针对Gentle AdaBoost算法构造的级联分类器检测效率偏低的问题，提出了一种递进复杂度的级联分类器。针对传统粒子滤波器最高权重粒子不准确的问题，提出了均值权重粒子滤波器。针对传统粒子滤波器样本衰退的问题，提出了一种同时结合人脸检测和人脸跟踪算法的跟踪校正策略。对于检测和跟踪到的人脸，利用基于Gabor变换和HMM的方法进行身份识别。实验结果表明，系统能够准确地检测并实时跟踪视频中的人脸，可以实现人脸的快速识别，是一种能够应用到视频监控系统中的有效方法。

关键词: 递进结构, 均值权重, 跟踪校正, Gabor特征, 隐马尔可夫模型（HMM）