Deep Cascaded Bi-Network for Face Hallucination

Zhu, Shizhan; Liu, Sifei; Loy, Chen Change; Tang, Xiaoou

doi:10.1007/978-3-319-46454-1_37

Cited by 209 publications

(171 citation statements)

References 49 publications

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“…In [62], a spatial alignment network is proposed for LR and HR matching. A cascaded bi-network is proposed in [66] for FH and deep reinforcement learning is applied in [2] to achieve attention awareness. The CNN generative framework usually handles input face images in an extremely low resolution where the facial components are not able to be distinguished.…”

Section: Face Hallucinationmentioning

confidence: 99%

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

et al. 2019

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We address the problem of restoring a highresolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.

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Section: Face Hallucinationmentioning

confidence: 99%

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

et al. 2019

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“…We compare our results quantitatively with the state-ofthe-art face hallucination methods CBN [52], WaveletSR [18], TDAE [48], GFRNet [27] and super-resolution methods SRCNN [10], VDSR [23], SRGAN [26]. For all those methods, we directly use the results reported in [27].…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Exemplar Guided Face Image Super-Resolution Without Facial Landmarks

Dogan

Timofte

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very lowresolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8× on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8× and outperforms state-of-theart in quantitative terms and perceptual quality.

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“…VGG-Net [1] is a powerful CNN that can be deployed for multiple applications including face recognition. And there are also eminent works that use CNN for face hallucination [6,7,8], which inspire our work to a large extent.…”

Section: B Convolutional Neural Networkmentioning

confidence: 96%

Cascaded Hallucination-Classification Deep Network for Low-Resolution Face Recognition in the Wild

Zhang¹,

Cheung²

2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Low-resolution face recognition (LR FR) has become an active research subarea due to its significances for real applications. Conventional low-resolution face recognition approaches meet challenges like noise affection and lack of effective features with LR faces. In this paper, we propose a deep learning method for LR FR. Our convolutional neural network (CNN) model directly learns an end-to-end classification on LR faces. Different from normal CNN for high-resolution (HR) face recognition, ours integrates a lightweight hallucination network mapping LR images into HR ones. Furthermore, we concatenate the hallucination and classification networks so that the training propagation is operated in one model, which largely boosts the performance over basic CNN and separate two-step models. Besides, our model is robust to varying poses and illuminations in the wild, and also portable to embedded system for its memoryand energy-saving features.

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Deep Cascaded Bi-Network for Face Hallucination

Cited by 209 publications

References 49 publications

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

Exemplar Guided Face Image Super-Resolution Without Facial Landmarks

Cascaded Hallucination-Classification Deep Network for Low-Resolution Face Recognition in the Wild

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