Synthetic Data Generation Approach for Face Recognition System

Autor:	Maksim Letenkov, Dmitrii Malov
Rok vydání:	2019
Předmět:	Computer science business.industry Machine learning computer.software_genre Head rotation Facial recognition system Synthetic data Deep neural networks Synthetic data generation Smart environment Artificial intelligence Transfer of learning business computer Classifier (UML)
Zdroj:	Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings” ISBN: 9789811392665
DOI:	10.1007/978-981-13-9267-2_41
Popis:	User identification within smart environments (SE) is intended to organize functioning of all services efficiently. One of the most popular ways to recognize a user is by his or her face. It is necessary to have a large dataset as it is possible to train an accurate classifier, especially if deep neural networks are being used. It is expensive to organize representative dataset manually to take a photograph of every person from every possible angle with every possible light condition. This is why the generation of synthetic data for training a classifier, using minimum real data is so urgent. In this paper, we propose an approach for the generation synthetic user’s face images with different head rotation and lighting conditions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::068a4e8d0f863344fc545a463da26878 https://doi.org/10.1007/978-981-13-9267-2_41 Zobrazit plný text záznamu