Abstract
This paper addresses two challenging tasks: detecting small faces in unconstrained conditions and improving the quality of very low-resolution facial images. Tiny faces are so fuzzy that the facial patterns are not clear or even ambiguous resulting in greatly reduced detection. In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN). Besides, we also designed a prior information estimation network which extracts the facial image features, and estimates landmark heatmaps respectively. By combining these two networks, we propose the end-to-end system that addresses both tasks simultaneously, i.e. both improves face resolution and detects the tiny faces. Extensive experiments on the challenging dataset WIDER FACE demonstrate the effectiveness of our proposed method in restoring a clear high-resolution face from a blurry small one, and show that the detection performance outperforms other state-of-the-art methods.
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Acknowledgment
This research was supported by the National Natural Science Foundation of China (Nos. 61672203 & 61976079) and Anhui Natural Science Funds for Distinguished Young Scholar (No. 170808J08).
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Liu, D., Zhao, ZQ., Tian, WD. (2020). TFPGAN: Tiny Face Detection with Prior Information and GAN. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_6
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DOI: https://doi.org/10.1007/978-3-030-60796-8_6
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