视频人脸识别中基于聚类中心LLE的特征相似性融合方法

针对大部分现有视频人脸识别方法通常仅利用代表性范例或图像集而较少研究有效融合的问题,提出了一种基于聚类中心特征相似性融合方法。首先,使用局部线性嵌入从原始数据空间学习低维嵌入,并利用STHAC算法将投影划分为LLE特征空间聚类;然后,从基于局部外观的聚类中得到特征相似性,在贝叶斯最大后验概率分类框架中对范例点和聚类子空间进行相关相似性匹配;最后,借助于范例重要性概率完成人脸的识别。在视频人脸数据集CMU Mobo、Honda/UCSD和ChokePoint上的实验验证了所提方法的有效性,实验结果表明,相比几种传统的方法,所提方法取得了较高的识别精度和较低的计算复杂度。

Feature similarities fusion method based on LLE in cluster-centric for video face recognition

A fusing method based on cluster-centric feature similarities is proposed. Firstly, locally linear embedding is used to learn low dimension embedding from original data space, and STHAC is used to divide projects as clustering in LLE feature space. Then, feature similarities are got from local appearance-based cluster, relevant similarity matching of exemplar points and cluster subspaces are done in a Bayesian maximum-a posteriori classification framework. Finally, face recognition is finished by importance probability of exemplars. The effectiveness of proposed method has been verified by experiments on video face databases CMU Mobo, Honda/UCSD and ChokePoint. Experimental results show that proposed method has higher recognition precision than several traditional methods.