Abstract
In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.
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Acknowledgments
This work was supported by the Chinese National Natural Science Foundation Projects #61473291, #61572536, #61572501, #61573356, the National Key Research and Development Plan (Grant No. 2016YFC0801002), and AuthenMetric R&D Funds.
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Guo, J., Zhu, X., Lei, Z., Li, S.Z. (2018). Face Synthesis for Eyeglass-Robust Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_30
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DOI: https://doi.org/10.1007/978-3-319-97909-0_30
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