提出了融合小波和貝葉斯的人臉識(shí)別方法。對(duì)原始圖像采用小波分解后，原始圖像被分解到不同的頻帶上。利用小波理論分析可知，在每一級(jí)分解中，低頻子圖像包含了原始圖像的主要描述信息，而其他高頻子圖像包含的信息較少，對(duì)模式分類(lèi)的作用也較小，所以可忽略不計(jì)。該算法首先對(duì)圖像進(jìn)行二級(jí)小波分解，其次對(duì)得到的每幅低頻子圖進(jìn)行貝葉斯人臉識(shí)別。在FERET 人臉庫(kù)的子集上對(duì)識(shí)別算法進(jìn)行了測(cè)試和比較。實(shí)驗(yàn)表明，與傳統(tǒng)的方法相比較，該方法降低了運(yùn)算量，提高了識(shí)別率。

關(guān)鍵詞：人臉識(shí)別；小波變換；貝葉斯方法
Abstract: A new algorithm for face recognition based on wavelet transform and Bayesian 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. Then, Bayesian approach is used to the
low-frequency sub-band. Its efficiency and superiority are clarified by comparative experiment on a subset of FERET face data.