中文摘要：

提出了基于小波变换和隐马尔可夫模型的人像鉴别算法．该算法首先对图像进行3级小波分解，然后把3个不同分辨率的低频子图像由小到大排列成树状结构，形成低频小波树．接着利用独立元分析对每个小波树枝进行去相关、降维，形成特征小波树枝，并把它作为观测向量对隐马尔可夫模型进行训练，把优化的模型参数用于人脸识别．分析了观测向量维数与识别率的关系，以及状态个数和高斯概率混合成分的个数对识别率的影响，定性描述了隐马尔可夫模型的本质．在ORL人脸数据库上，同其他4种相关方法进行了比较，实验结果表明，该方法识别率较高，工程上易于应用．

英文摘要：

A new algorithm for face recognition based on wavelet transform and hidden Markov model （HMM） is proposed. Three low frequency sub-band images are selected by applying three-level wavelet transform. The low frequency wavelet sub-trees are formed by arranging three low frequency images in order. The feature wavelet subtree branches, as observation vectors of HMM, are derived by using independent component analysis. A set of images representing different instances of the same person is used to train each HMM. The relationship between the dimensionality of observation vectors and the recognition rates is shown. The effect of the number of states and Gaussian kernels on the system performance is examined. The essence of HMM is described qualitatively. Experimental results on the ORL face dataset are compared with other published algorithms, and show that the proposed algorithm has a high recognition rate with a good perspective.