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Lossy face image compression can degrade the image quality and the utilityfor the purpose of face recognition. This work investigates the effect of lossyimage compression on a state-of-the-art face recognition model, and on multipleface image quality assessment models. The analysis is conducted over a range ofspecific image target sizes. Four compression types are considered, namelyJPEG, JPEG 2000, downscaled PNG, and notably the new JPEG XL format. Frontalcolor images from the ColorFERET database were used in a Region Of Interest(ROI) variant and a portrait variant. We primarily conclude that JPEG XL allowsfor superior mean and worst case face recognition performance especially atlower target sizes, below approximately 5kB for the ROI variant, while thereappears to be no critical advantage among the compression types at highertarget sizes. Quality assessments from modern models correlate well overallwith the compression effect on face recognition performance.

Effect of Lossy Compression Algorithms on Face Image Quality and Recognition