Deep Learning-based Compression Detection for explainable Face Image Quality Assessment

TL;DR

This paper presents a deep learning approach using neural networks to detect compression artefacts in face images, providing explainable quality assessment to improve face recognition reliability.

Contribution

It introduces a neural network-based method trained with PSNR and SSIM labels to detect JPEG and JPEG 2000 artefacts, enhancing face image quality evaluation.

Findings

Detection error rates of 2-3% for compression artefacts.

Significant reduction in face recognition errors by discarding severely compressed images.

EfficientNetV2-based algorithm available as open-source software.

Abstract

The assessment of face image quality is crucial to ensure reliable face recognition. In order to provide data subjects and operators with explainable and actionable feedback regarding captured face images, relevant quality components have to be measured. Quality components that are known to negatively impact the utility of face images include JPEG and JPEG 2000 compression artefacts, among others. Compression can result in a loss of important image details which may impair the recognition performance. In this work, deep neural networks are trained to detect the compression artefacts in a face images. For this purpose, artefact-free facial images are compressed with the JPEG and JPEG 2000 compression algorithms. Subsequently, the PSNR and SSIM metrics are employed to obtain training labels based on which neural networks are trained using a single network to detect JPEG and JPEG 2000 artefacts, respectively. The evaluation of the proposed method shows promising results: in terms of detection accuracy, error rates of 2-3% are obtained for utilizing PSNR labels during training. In addition, we show that error rates of different open-source and commercial face recognition systems can be significantly reduced by discarding face images exhibiting severe compression artefacts. To minimize resource consumption, EfficientNetV2 serves as basis for the presented algorithm, which is available as part of the OFIQ software.