Abstract
Face frontalization is the process of synthesizing realistic and identity-preserving frontal views from face images in different poses and is an essential preprocessing step for face recognition. However, for side faces wearing glasses, the previous frontalization algorithms will distort the glasses after face reconstruction, affecting the image’s perceived quality and subsequent face recognition. Therefore, this paper first removes glasses, a factor that will cause distortion in face frontalization, and designs the perceptual and pixel-level face image quality assessment modules to improve the face frontalization performance. On the one hand, by constructing a saliency gradient, the pixel-level quality of face images is calculated and guides the network to generate frontal face images that are more conducive to face recognition. On the other hand, in order to obtain the perceptual quality for face image, the natural face images are used to construct a high-quality feature space, and the Bhattacharyya distance between it and the generated image is calculated to ensure the perceptual quality of the generated frontal image. Finally, the GAN network is used to generate a frontal face image that can consider both recognizability and perceptual quality. Quantitative and qualitative evaluations on controlled and in-the-wild databases show that our method outperforms the state-of-the-art.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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The research leading to these results received funding from the Basic Science (Natural Science) Research Projects of Universities in Jiangsu Province under Grant Agreement No[22KJB520011].
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by XD. HL and JL provided supervision. The first draft of the manuscript was written by XD, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acquisition of the financial support for the project leading to this publication.
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Liu, H., Duan, X. & Liang, J. FF-PPQA: Face frontalization without glasses based on perceptual quality and pixel-level quality assessment. SIViP 18, 2879–2893 (2024). https://doi.org/10.1007/s11760-023-02957-6
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DOI: https://doi.org/10.1007/s11760-023-02957-6