提出了基于奇异值特征和隐马尔可夫模型（HMM）的人脸检测方法，首先提出了基于奇异值特征和隐马尔可夫模型的正面端正人脸检测方法；然后将该算法扩展到检测任意旋转角度的人脸，其中正向端正人脸检测算法是通过隐马尔可夫模型来识别人脸／非人脸的奇异值特征，从而达到人脸检测的目的；扩展算法首无计算当前位置子图象窗口的奇异值特征向量，然后利用识别各个旋转角度人脸的HMM模型对之进行分类，以得到该子图象窗口的旋转角度，再经过旋正，重新再与识别正面端正人脸的HMM模型对, 此确定该子图象窗口是否为人脸，通过对一个由51幅集体照片组成的图象集进行测试，其中，正面端正人脸检测率为85．1％，而任意旋转角度的人脸检测率只有72．2％。

This paper presents a novel face detection algorithm, which consists of two parts of research work. The first one is a frontal view upright face detection algorithm which is based on the well known singular value feature (SVF) and hidden Markov models (HMM). A trained HMM is employed to classify the SVF of the sub-image at every location in the image as a face or nonface. The algorithm couples the virtues of both the SVF and HMM and produces excellent detection results. It is tested on a collect photo album and has detected the 85.1 percent of its 484 people, while 97 false alarms are also reported. The second part of our algorithm is the extension of the first one to rotation invariant face detection. Several HMMs are utilized to recognize the SVF of the sub image at the same time to obtain the angle of the "face" image. Then the HMM for detecting the upright faces is employed to verify the faceness of the rotated test pattern. The rotation invariant algorithm is tested on another image set where there are 173 persons. The detection rate is 72 2%,which seems not too high,but the false alarm rate is also low, only 34 out of the 6 100 000 windows,scanned.