基于改进PCA算法的人脸识别

Face Recognition Based on Improved PCA Algorithm

由于经典的PCA算法要求样本满足高斯分布,然而现实中的样本往往因为表情、角度、光照等原因不满足高斯分布,导致算法识别率不高。因此,提出一种基于改进PCA算法的人脸识别方法。首先,将具有相似特征(表情、角度、亮度)的不同样本通过分块方式划分在一个矩阵中,使样本趋于高斯分布;其次,通过直方图均衡化样本的方法,加强样本对比度,以突出样本的人脸器官特征;最后采用经典PCA算法进行辨识。通过在ORL人脸库上的实验得出,该方法不但耗费总时间少于经典的PCA算法,而且识别率也得到提升,具有一定可行性。

The classical PCA algorithm requires the sample to satisfy the Gaussian distribution, but the real samples often do not satisfy the Gaussian distribution because of the expression, the angle and the light. So the recognition rate of this algorithm is not high. For this reason, this paper presents a face recognition method based on improved PCA algorithm. Firstly, different samples with similar characteristics （expression, angle, brightness） are divided into a matrix by way of block in order to make samples tend to Gaussian distribution. Secondly, through the method of histogram to equalize the sample, the contrast of the sample is enhanced to highlight the facial features. Finally, the classical PCA algorithm is used to identify the samples. And through the experiment on the ORL face database, this method not only cost less total time than the classic PCA algorithm and recognition rate has also been improved. In a general, this way is feasible.