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.
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Yin, H., Meng, S., Yu, H. (2022). Face Virtual Sample Generation with 3D Model Based on Generative Adversarial Networks. In: Pan, JS., Meng, Z., Li, J., Virvou, M. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 278. Springer, Singapore. https://doi.org/10.1007/978-981-19-1053-1_8
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DOI: https://doi.org/10.1007/978-981-19-1053-1_8
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