基于深浅特征融合的人脸识别

Fusion of deep and shallow features for face recognition

针对传统的浅层特征所提取特征的判别性有限、深度特征需要大量带标记样本且训练过程耗时长的问题,提出一种深度及浅层特征融合算法用于人脸识别。首先提取人脸的HOG特征并进行判别性降维;同时,提取人脸图像的PCANet特征并降维;其次,将降维后的深浅特征进行融合,并进一步提取判别性特征;最后,采用SVM分类器进行分类并在AR和Yale B数据库上对算法进行验证。实验结果证明,该算法能够比单独选用深度特征和浅层特征进行分类达到更高的识别率,且对特征维数具有更强的鲁棒性。

The traditional method for feature extraction contains limited discriminant features,and the deep learning method need lots of labeled data and it′s time-consuming.This paper presents a method which fuses the deep and shallow features for face recognition.Firstly,the HOG feature is extracted from each images and the dimensionality reduction is followed;and the PCANet feature is extracted simultaneously and its′dimension is reduced.Secondly,the fusion of the two types of features is conducted and discriminant features are extracted further.Finally,the SVM is adopted for classification.Experiments on the AR database verify the effectiveness and robustness of the proposed method.