The article is considering the problem of increasing the performance and accuracy of video face identification. We examine the selection of the several best video frames using various techniques for assessing the quality of images. In contrast to traditional methods with estimation of image brightness/contrast, we propose to utilize the deep learning techniques that estimate the frame quality by using the lightweight convolutional neural network. In order to increase the effectiveness of the frame quality assessment step, we propose to distill knowledge of the cumbersome existing FaceQNet model for which there is no publicly available training dataset. The selected K-best frames are used to describe an input set of frames with a single average descriptor suitable for the nearest neighbor classifier. The proposed algorithm is compared with the traditional face feature extraction for each frame, as well as with the known clustering methods for a set of video frames.

中文翻译：

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基于帧选择和质量评估的高效视频人脸识别

本文正在考虑提高视频面部识别的性能和准确性的问题。我们使用各种评估图像质量的技术来检查几种最佳视频帧的选择。与估计图像亮度/对比度的传统方法相反，我们建议利用深度学习技术，通过使用轻量级卷积神经网络来估计帧质量。为了提高帧质量评估步骤的有效性，我们建议提取不存在公开可用的训练数据集的繁琐的现有FaceQNet模型的知识。所选的K最佳帧用于描述具有适用于最近邻居分类器的单个平均描述符的一组输入帧。