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Improving the Neural Network Algorithm for Assessing the Quality of Facial Imagesстатья
- Авторы: Lisin Nikita, Gromov Alexander, Konushin Vadim, Konushin Anton
- Сборник: Proceedings of the 30th International Conference on Computer Graphics and Machine Vision
- Серия: CEUR Workshop Proceedings
- Том: 2744
- Год издания: 2020
- Издательство: M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen
- Местоположение издательства: Aachen, Germany
- Первая страница: 1
- Последняя страница: 12
- DOI: 10.51130/graphicon-2020-2-3-28
- Аннотация: The paper considers the task of obtaining a quality assessment of facialimages for usage in various video surveillance systems, video analytics and biometric identification. Accuracy of person recognition and classification depends on the quality of the input images. We consider an approach to obtaining single face image quality assessment using neural network model, which is trained onpairs of images that are split into two possible classes: the quality of the firstimage is better or worse than the quality of the second one. Two modificationsof the selected baseline algorithm are proposed. A face recognition system is applied to change the loss function and image and face quality attributes are usedwhen training the model. Experimental studies of the proposed modificationsshow their effectiveness. The accuracy of selecting the best and worst frame isincreased by 1.3% and 1.9%, respectively.
- Добавил в систему: Конушин Антон Сергеевич