# Effect of Lossy Compression Algorithms on Face Image Quality and Recognition

arXiv: https://arxiv.org/abs/2302.12593 · HTML: https://mlsanity.com/paper/2302.12593/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability](https://mlsanity.com/paper/2112.06592/) (2021)
- [FaceQnet: Quality Assessment for Face Recognition based on Deep Learning](https://mlsanity.com/paper/1904.01740/) (2019)
- [Biometric Quality: Review and Application to Face Recognition with FaceQnet](https://mlsanity.com/paper/2006.03298/) (2020)

## Built on by (most cited first, 2 of 2)

- [On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection](https://mlsanity.com/paper/2604.20585/) (2026)
- [Optimizing Image Preparation and Compression for Face Recognition within 1024 Bytes](https://mlsanity.com/paper/2606.30321/) (2026)

## Abstract (arXiv)

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.