🤖 AI Summary
JPEG and JPEG 2000 compression degrades facial image fidelity, leading to reduced face recognition accuracy.
Method: This paper proposes an interpretable, pre-processing quality control framework for facial image quality assessment. It unifies detection of artifacts from both compression standards—using PSNR/SSIM-derived weak supervision labels—and trains a lightweight, end-to-end EfficientNetV2 binary classifier without manual annotation. The model supports real-time deployment and achieves a compression-type classification error rate of only 2–3%.
Contribution/Results: Extensive evaluation across multiple open-source and commercial face recognition systems demonstrates that filtering out high-artifact images significantly reduces downstream recognition error rates. The proposed method has been integrated into the open-source OFIQ framework, providing actionable, interpretable quality feedback to enhance robustness in face recognition pipelines.
📝 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.