Going the Extra Mile in Face Image Quality Assessment : A Novel Database and Model

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2024
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Sun, Jinqiu
Zhu, Yu
Liu, Hantao
Zhang, Yanning
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Deutsche Forschungsgemeinschaft (DFG): 251654672 – TRR 161
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IEEE Transactions on Multimedia. Institute of Electrical and Electronics Engineers (IEEE). 2024, 26, pp. 2671-2685. ISSN 1520-9210. eISSN 1941-0077. Available under: doi: 10.1109/tmm.2023.3301276
Zusammenfassung

An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the study of face images is an important subfield in computer vision research, the lack of face IQA data and models limits the precision of current IQA metrics on face image processing tasks such as face superresolution, face enhancement, and face editing. To narrow this gap, in this article, we first introduce the largest annotated IQA database developed to date, which contains 20,000 human faces – an order of magnitude larger than all existing rated datasets of faces – of diverse individuals in highly varied circumstances. Based on the database, we further propose a novel deep learning model to accurately predict face image quality, which, for the first time, explores the use of generative priors for IQA. By taking advantage of rich statistics encoded in well pretrained off-the-shelf generative models, we obtain generative prior information and use it as latent references to facilitate blind IQA. The experimental results demonstrate both the value of the proposed dataset for face IQA and the superior performance of the proposed model.

Zusammenfassung in einer weiteren Sprache
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004 Informatik
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Image quality assessment, face quality, subjective study, GAN, generative priors
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ISO 690SU, Shaolin, Hanhe LIN, Vlad HOSU, Oliver WIEDEMANN, Jinqiu SUN, Yu ZHU, Hantao LIU, Yanning ZHANG, Dietmar SAUPE, 2024. Going the Extra Mile in Face Image Quality Assessment : A Novel Database and Model. In: IEEE Transactions on Multimedia. Institute of Electrical and Electronics Engineers (IEEE). 2024, 26, pp. 2671-2685. ISSN 1520-9210. eISSN 1941-0077. Available under: doi: 10.1109/tmm.2023.3301276
BibTex
@article{Su2024Going-69906,
  year={2024},
  doi={10.1109/tmm.2023.3301276},
  title={Going the Extra Mile in Face Image Quality Assessment : A Novel Database and Model},
  volume={26},
  issn={1520-9210},
  journal={IEEE Transactions on Multimedia},
  pages={2671--2685},
  author={Su, Shaolin and Lin, Hanhe and Hosu, Vlad and Wiedemann, Oliver and Sun, Jinqiu and Zhu, Yu and Liu, Hantao and Zhang, Yanning and Saupe, Dietmar}
}
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