@inproceedings{082b7eab58724a1db3efc507faf5aa1a,
title = "Face Virtual Sample Generation with 3D Model Based on Generative Adversarial Networks",
abstract = "Generating virtual multi-view face images from a single face image has always been a challenge in the area of computer vision. It often suffers from appearance distortions and artifacts if the generation rule is not well-defined. In order to generate virtual samples that can contribute to recognition accuracy, this paper proposes a three-dimensional face model reconstruction method based on generative adversarial networks to expand the training dataset. It transforms single training sample per person face recognition into multi-training sample face recognition by expanding the training dataset. Our generated virtual samples have been proved efficient in improving recognition rate on face database using different algorithms.",
keywords = "Generative adversarial networks, Single sample face recognition, Three-dimensional face model",
author = "Hongtao Yin and Shengwei Meng and Han Yu",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2021, in conjunction with the 14th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2021 ; Conference date: 29-10-2021 Through 31-10-2021",
year = "2022",
doi = "10.1007/978-981-19-1053-1\_8",
language = "英语",
isbn = "9789811910524",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "79--89",
editor = "Jeng-Shyang Pan and Zhenyu Meng and Jianpo Li and Maria Virvou",
booktitle = "Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceeding of the IIH-MSP 2021 and FITAT 2021",
address = "德国",
}