基于均匀k均值和高维局部二值模式的人脸识别算法

Face recognition algorithm based on homogeneous k-means and highdimensional local binary pattern

针对传统局部二值模式(LBP)及其一些改进方法会将具有不同灰度特征的邻域赋予相同的特征值和特征维数倍增的问题,提出一种基于均匀k均值和高维局部二值模式的算法.该算法首先对原图进行切割得到子图;然后提取子图的高维局部二值模式特征,利用均匀k均值对高维特征进行降维处理;最后级联所有的子图特征进行分析.为了验证该算法的性能,在ORL人脸库和YALE人脸库以及FERET人脸库上进行对比实验,结果表明该算法在保证特征维数不递增的前提下,能够明显提高LBP算法的识别率.

In view of the problem that the traditional local binary pattern（LBP） and its extensions give the same eigenvalues and multiplication of feature dimension to the neighborhoods with different gray features, an algorithm based on the homogeneous k-means and high-dimensional local binary pattern is proposed. Firstly, the algorithm gets the sub-graph by cutting the original image, then extracts the high-dimensional local binary pattern characteristics of sub-graph and uses the homogeneous k-means to process the high-dimensional features by dimension reduction. Finally, the features of all the sub-graphs are cascaded to be analyzed. To verify the performance of the algorithm, the comparative experiments on the ORL face database, YALE face database and FERET face database are conducted, and the results show that the algorithm obviously improve the recognition rate of the LBP algorithm on the premise of ensuring that the feature dimension doesn＇t increase.