基于小波变换和隐马尔可夫模型的人脸识别方法

提出了基于小波变换和隐马尔可夫模型的人脸识别方法。对原始图像采用小波分解后,原始图像被分解到不同的频带上。利用小波理论分析可知,在每一级分解中,低频子图像包含了原始图像的主要描述信息,而其他3个高频子图像包含的信息较少,对模式分类的作用也较小,所以可忽略不计。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。接着利用主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,实验结果表明,该方法识别率较高,具有很好的发展前景。

Face Recognition Based on Wavelet Transform and Hidden Markov Model

A new algorithm for face recognition based on wavelet transform and hidden Markov model(HMM) is proposed.The original image is decomposed into low frequency and high frequency sub-band images by applying wavelet transform.According to the wavelet theory,the low frequency image is the smoothed version of the original image and the best approximation to the original image with lower-dimensional space.It also contains main energy content within the original image.But the other high frequency sub-band images contain less energy content,and are almost useless to pattern discrimination.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 sub-tree branches,as observation vectors of HMM,are derived by using principal component analysis.A set of images representing different instances of the same person are used to train each HMM.The feasibility of the algorithm has been successfully tested on the ORL face dataset.Experimental results show that the proposed algorithm has a good perspective.