基于相似度分布的开集人脸识别方法

利用相似度多个维度的信息进行开集判别，以提高开集人脸识别的准确率。该方法首先通过大量带标识的测试样本获得已知类样本和非已知类样本相似度向量的分布，然后引入线性判别分析学习两个类中相似度向量的分布特征，在开集判别中通过相似度向量的特征匹配来判断样本是否为已知类。利用相似度分布中的分类信息，训练出的特征具有更强的分类能力。不同人脸库的实验表明，相对于传统方法，文中方法能提高开集识别的准确率。

Open Set Face Recognition Approach Based on Similarity Distribution

A classification algorithm based on multi-dimensional similarity distribution is presented to enhance the accuracy in open set face recognition. This algorithm firstly get the similarity vector distribution of known and unknown samples by testing on many labeled pictures. Then those similarity vectors are learned by linear discriminant analysis (LDA) to extract distribution features. Finally, the proposed algorithm rejects the unknown identity by feature-matching. Hence, the feature has strong classification ability in view of the discrimination information abstracted from the similarity distribution. Experimental results on several face databases demonstrate that the proposed method significantly outperforms the traditional method for open set face recognition.