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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2914039
|2 doi
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|a pubmed24n0989.xml
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Yu, Xin
|e verfasserin
|4 aut
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| 245 |
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|a Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a Date Completed 16.02.2021
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|a Date Revised 16.02.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In popular TV programs (such as CSI), a very low-resolution face image of a person, who is not even looking at the camera in many cases, is digitally super-resolved to a degree that suddenly the person's identity is made visible and recognizable. Of course, we suspect that this is merely a cinematographic special effect and such a magical transformation of a single image is not technically possible. Or, is it? In this paper, we push the boundaries of super-resolving (hallucinating to be more accurate) a tiny, non-frontal face image to understand how much of this is possible by leveraging the availability of large datasets and deep networks. To this end, we introduce a novel Transformative Adversarial Neural Network (TANN) to jointly frontalize very-low resolution (i.e., 16 × 16 pixels) out-of-plane rotated face images (including profile views) and aggressively super-resolve them (8×), regardless of their original poses and without using any 3D information. TANN is composed of two components: a transformative upsampling network which embodies encoding, spatial transformation and deconvolutional layers, and a discriminative network that enforces the generated high-resolution frontal faces to lie on the same manifold as real frontal face images. We evaluate our method on a large set of synthesized non-frontal face images to assess its reconstruction performance. Extensive experiments demonstrate that TANN generates both qualitatively and quantitatively superior results achieving over 4 dB improvement over the state-of-the-art
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Shiri, Fatemeh
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ghanem, Bernard
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Porikli, Fatih
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 42(2020), 9 vom: 14. Sept., Seite 2148-2164
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:42
|g year:2020
|g number:9
|g day:14
|g month:09
|g pages:2148-2164
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2019.2914039
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