监督局部线性嵌入在人脸识别中的应用

Application of Gobor wavelet and SLLE in face recognition

为了提高人脸识别算法的识别率,提出了一种Gabor小波与监督局部线性嵌入(Supervised Locally Linear Embedding,SLLE)相结合的人脸特征提取算法。针对SLLE不能有效消除图像信息中冗余的高阶相关性,算法首先采用Gabor小波对人脸图像进行多方向、多分辨率滤波,提取图像在不同空间频率上的特征;然后采用监督的局部线性嵌入算法对该Gabor特征进行维数约简。在ORL和YALE人脸库上的实验显示,就算法有效提高了人脸识别的识别率。

In order to improve the recognition rate of face recognition algorithm, a new algorithm of face recognition is proposed based on Gabor wavelet transform and Supervised Locally Linear Embedding （SLLE）. Gabor wavelet is introduced as a method to extract Gabor magnitude features by convolving the normalized face image with multi-scale and muhi-orientation Gabor filters. In the feature extraction module, the dimension of Gabor features is reduced by SLLE. A minimum-distance classifier is trained for classification. With the test of the ORL and YALE face database, it is found that 3.5 %-37.8% increase in recognition rate can be achieved compared with other algorithms.