基于兴趣点定位的局部方向模式人脸识别方法

Improved location direction pattern based on interest points location for face recognition

为了解决局部方向模式(LDP)在人脸特征提取过程中采用固定的平均分块方式,不能自适应突出不同样本特征的这一问题,提出一种基于兴趣点定位的改进LDP人脸特征提取方法。兴趣点所在位置特征信息丰富,其根据不同图像自动分布,可以突出不同图像的不同特点。首先定位人脸图像的加速鲁棒特征(SURF)特征点,并通过K-means聚类算法优化兴趣点的数量,确定兴趣点位置;之后以每个兴趣点作为中心建立LDP特征提取窗口,计算其4方向LDP编码,得出图像的特征向量;最后,采用支持向量机(SVM)对人脸进行识别分类。使用该改进算法分别在FERET和Yale数据库中进行实验,并与原始LDP、4方向的LDP方法 (4-LDP)、融合PCA与LDP的特征提取算法(PCA-LDP)进行了比较,实验结果表明,所提出的特征提取方法在保证系统实时性的同时,可以有效提高人脸识别的准确率与稳定性。

In order to solve the problem that Local Directional Pattern（ LDP） adopts the fixed average block method in the face feature extraction process, which cannot reflect the characteristics of different images well, an improved LDP based on interest point location was proposed. The positions of interest points contained rich feature information, and the interest points could be obtained automatically according to particular image. Firstly, the locations of interest points were decided by Speed Up Robust Feature（ SURF） algorithm and K-means clustering algorithm. Secondly, 4-direction LDP（ 4-LDP） coding was calculated by the feature extraction windows established with each interest point as the center. Finally, the Support Vector Machine（ SVM） was used to identify the face. The proposed method was evaluated in Yale and FERET databases and compared with the original LDP, 4-LDP and PCA-LDP（ feature extraction method combined Principal Component Analysis and LDP）. The experimental results show that the proposed method can obviously improve the recognition rate and stability while ensuring the real-time performance of the system.