Efficient video face recognition based on frame selection and quality assessment

Abstract

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.

A Comprehensive Survey on Face Quality Detection in a Video Frame

“…In this study, the problem of occlusion is solved and faces that are either completely or partially obscured in the videos are identified. Angelina Kharchevnikova [6] A lightweight convolutional neural network has been created to evaluate the frame quality using DL methods. We propose extracting knowledge from the clumsy existing Face Q-Net model, for which there is no publicly available training dataset, in order to improve the stage's effectiveness when we assess the frame quality.…”

“…The model can monitor its accuracy and progress over time because the inputs and outputs are labelled. Both classification and regression are supervised learning techniques that can be used with data mining [10]. An algorithm is used to tackle classification issues, such as distinguishing between apples and oranges, and accurately divide test data into multiple categories.…”

Face Recognition Bias Assessment through Quality Estimation Models

“…The scoring mechanism involves computing scores using the Wasserstein distance between the distributions of similarities among images from the same user and different users. A larger distance between these two distributions results in a higher quality score [11]. Finally, a facerecognition model is trained using the obtained labels, similar to the approach in FaceQNet.…”