Attention-guided cascaded networks for improved face detection and landmark localization under low-light conditions

Victor Oludare; Landry Kezebou; Karen Panetta; Sos Agaian

doi:10.1117/12.2558397

21 April 2020 Attention-guided cascaded networks for improved face detection and landmark localization under low-light conditions

Victor Oludare, Landry Kezebou, Karen Panetta, Sos Agaian

Author Affiliations +

Proceedings Volume 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020; 113990J (2020) https://doi.org/10.1117/12.2558397
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Often, state-of-the-art techniques for face detection exhibit suboptimal performance under poor illumination conditions. For instance, the absence of ambient light effectively challenges a model’s capacity to extract relevant global and local features needed to localize faces in images. This paper proposes a technique based on generative adversarial networks to improve the visual quality of images captured in low lighting conditions to significantly improve face detection and landmark localization in real-world images. An attention-guided cascaded residual generator network (AG-CRN) along with a Markovian discriminator is trained in an adversarial manner to synthesize enhanced and properly illuminated images given corresponding low-light pairs. Extensive experiments on several datasets, including the DARK FACE dataset, demonstrate that AG-CRN is capable of producing visually pleasing images, and also significantly improves the performance of state-of-the-art models for face detection and landmark localization under very low-lighting conditions.

Citation Download Citation

Victor Oludare, Landry Kezebou, Karen Panetta, and Sos Agaian "Attention-guided cascaded networks for improved face detection and landmark localization under low-light conditions", Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990J (21 April 2020); https://doi.org/10.1117/12.2558397

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