Deep Cascaded Bi-Network for Face Hallucination

18 Jul 2016  ·  Shizhan Zhu, Sifei Liu, Chen Change Loy, Xiaoou Tang ·

We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks localization or dense correspondence field), we alternatingly optimize two complementary tasks, namely face hallucination and dense correspondence field estimation, in a unified framework. In addition, we propose a new gated deep bi-network that contains two functionality-specialized branches to recover different levels of texture details. Extensive experiments demonstrate that such formulation allows exceptional hallucination quality on in-the-wild low-res faces with significant pose and illumination variations.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Super-Resolution WebFace - 8x upscaling CBN PSNR 23.10 # 5

Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Image Super-Resolution VggFace2 - 8x upscaling CBN PSNR 21.84 # 5


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