融合2DPCA和贝叶斯的人脸识别算法

Bayesian Face Recognition Using 2DPCA

提出了融合2DPCA和贝叶斯的人脸识别方法。首先用2DPCA方法进行识别,选择得分前10名的图像作为候选图像,然后对候选图像和测试图像进行小波分解,对得到的高频与低频子图并行进行贝叶斯人脸识别,通过加权排序得到最后结果。通过在FERET人脸库上的实验表明,与传统的方法相比较,该方法降低了运算量,提高了识别率。

A novel Bayesian approach to face recognition based on 2DPCA is proposed. Firstly the system uses 2DPCA to select the 10 top-ranked candidate images, secondly each testing image and all the candidate images are decomposed into low frequency and high frequency sub-band images by applying wavelet transform, Bayesian recognition is parallel processed using these sub-band im- ages. The face recognition result was gained through weigh-adding arraying. Its efficiency and superiority are clarified by comparative experiment on a subset of FERET face data.