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Effect of Lossy Compression Algorithms on Face Image Quality and Recognition

2023-02-24 12:11:05
Torsten Schlett, Sebastian Schachner, Christian Rathgeb, Juan Tapia, Christoph Busch

Abstract

Lossy face image compression can degrade the image quality and the utility for the purpose of face recognition. This work investigates the effect of lossy image compression on a state-of-the-art face recognition model, and on multiple face image quality assessment models. The analysis is conducted over a range of specific image target sizes. Four compression types are considered, namely JPEG, JPEG 2000, downscaled PNG, and notably the new JPEG XL format. Frontal color images from the ColorFERET database were used in a Region Of Interest (ROI) variant and a portrait variant. We primarily conclude that JPEG XL allows for superior mean and worst case face recognition performance especially at lower target sizes, below approximately 5kB for the ROI variant, while there appears to be no critical advantage among the compression types at higher target sizes. Quality assessments from modern models correlate well overall with the compression effect on face recognition performance.

Abstract (translated)

有损人脸识别压缩会对图像质量和人脸识别功能产生不利影响。这项工作研究了有损图像压缩对最先进的人脸识别模型以及多个面部图像质量评估模型的影响。分析涵盖了具体的图像目标大小范围。考虑了四种压缩类型:JPEG、JPEG 2000、downscaled PNG以及新生成的JPEG XL格式。从ColorFERET数据库中获取的前方颜色图像被用于ROI变异体和肖像变异体。我们的主要结论是:JPEG XL可以在较低的目标大小下提供更好的平均和最坏人脸识别性能,特别是在ROI变异体目标大小低于约5kB的情况下,而其他压缩类型在更高的目标大小下似乎没有显著的竞争优势。现代模型的质量评估与压缩对人脸识别性能的影响Overall很好地相关。

URL

https://arxiv.org/abs/2302.12593

PDF

https://arxiv.org/pdf/2302.12593.pdf


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