基于CNN的监控视频中人脸图像质量评估

在公共安全领域，监控视频中的人脸识别技术是不可或缺的技术，成为研究热点.而监控视频中低质量的人脸图像会大大降低整个人脸识别系统的识别准确率，系统难以更广泛地被投入实际使用.本文提出了一种基于CNN的人脸图像质量评估方法.通过对Alexnet模型进行改进，将网络中的多个卷积层与全连接层连接，从而提取不同尺度的图像特征.通过端到端的训练过程，预测人脸图像质量分数.另外，采用人脸识别算法来标定人脸图像的质量分数，使质量分数能更有效地筛选出适合识别算法的图像.在Color FERET数据集上实验表明，本文方法能够准确地对人脸图像进行质量评估.而在实际采集的监控视频数据集上实验表明，本文方法能筛选出高质量的人脸图像用作后续人脸识别，提高人脸识别准确率.

Face Image Quality Assessment in Surveillance Videos Using CNN

Face recognition in surveillance videos is an essential technology in public security and has gotten more and more attention. But it is a little hard for the face recognition systems to be integrated into real application due to the low recognition rate caused partly by low face image quality. This study proposes a method of face image quality assessment using CNN. The proposed net, modified from the Alexnet, connects intermediate convolution layers to fully connect layer, to get multiple image features. Then, face image quality scores can be gotten from proposed net which is trained by end to end. In addition, a face image quality metric is used to relate the quality with the face recognition algorithm. Experiments on Color FERET datasets show that the proposed algorithm is able to elevate the face image quality exactly. Further experiments on a video surveillance dataset (collected by ourselves) show that the proposed method can select high quality face image for face recognition, leading to significant improvements in recognition accuracy.