一种基于多种特征融合的人脸识别算法

A Face Recognition Algorithm Using Fusion of Multiple Features

为了进一步提高词袋模型在人脸识别中的性能,提出一种融和多种特征所建立的词袋模进行人脸识别的算法.首先提取人脸图像中的若干局部特征,分别基于每种特征离线训练视觉词典,将每种局部特征映射到对应的高维中层语义空间中,然后使用空间金字塔模型得到每种特征的人脸图像描述,最后将各种特征拼接起来并使用线性SVM完成对人脸图像的分类判别.在多个公开数据库上的实验结果表明,该算法对人脸的姿态、表情变化以及面部遮挡具有更优良的鲁棒性,能够更好地解决小样本问题.

In order to improve the performances of bag-of-features(BoF)in face recognition,a face recognition algorithm based on the fusion of multiple features under the framework of bag-of-features was proposed in this paper.This method firstly extracted different kinds of local features in face images to learn a corresponding over complete visual dictionary in advance.Then it mapped each local feature into a high dimensional mid-level semantic space,and employed spatial pyramid matching(SPM)to pool local coding features.Different kinds of features were concatenated as the final representation of images and classified by trained linear SVM.Experimental results on several benchmark datasets show that our method is more robust to position variations,expression changes and occlusion and can effectively solve the small training size problem.