低分辨率人脸图像的迭代标签传播识别算法

针对监控视频下低分辨率人脸识别中存在的特征表示能力不强及判别开集人脸图像身份不够准确的问题,文中提出低分辨率人脸图像的迭代标签传播识别算法.采用视觉几何组(VGG)提取人脸图像特征,依据特征的相似度获得高、低分辨率图像的映射关系.对已标记样本和未标记样本进行迭代标签传播,在每次迭代过程中,通过统计每个类别的置信度直方图,估计识别精确率逼近100%的自适应置信度阈值.根据阈值将确认的未标记样本更新至已标记样本集,提高算法召回率.在公共数据集上的实验表明,文中算法在精确率逼近100%的基础上,召回率取得较高值.

Low-Resolution Face Recognition Based on Recursive Label Propagation Algorithm

In low-resolution face recognition, the feature representation ability is not robust and the discrimination result of open-set face recognition is inaccurate. Therefore, an algorithm for low-resolution face recognition based on recursive label propagation algorithm is proposed. Firstly, VGG network is utilized to extract face representations. Secondly, the mapping relationship between high-resolution and low-resolution images can be acquired based on the similarity of feature vectors. Finally, the iteration label propagation algorithm is conducted on the labeled and unlabeled facial samples. During the iterations, the adaptive confidence threshold approaching to 100% recognition accuracy is estimated according to the confidence histogram of each category. The identified unlabeled samples are updated to the labeled sample set based on the threshold, thereby the recognition recall rate is improved. Experimental results on public face datasets show that the proposed algorithm achieves a high recall rate with 100% precision.