Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality

2026-06-10

AI Summary

This study addresses the challenge of identity document image quality assessment in remote identity verification, which critically limits the performance of presentation attack detection (PAD). For the first time, it systematically incorporates capture-related quality metrics from the Open Face Image Quality (OFIQ) standard into identity document image evaluation. The proposed approach employs preprocessing steps—including corner detection, perspective normalization, and foreground masking—to produce accurate and unbiased quality scores. Extensive experiments across four diverse identity document datasets demonstrate that the selected OFIQ metrics substantially enhance the performance of three state-of-the-art PAD methods, thereby establishing a clear and quantifiable relationship between image quality and PAD efficacy.

Abstract

This paper addresses the challenge of assessing image quality in ID cards in remote verification systems by applying capture-related quality measures from the Open Face Image Quality (OFIQ) standard to ID card images. Our preprocessing pipeline includes corner detection, perspective normalization, and comprehensive foreground masking to ensure accurate and unbiased quality measure computation. We evaluate the effectiveness of these measures by analyzing their correlation with the performance of three presentation attack detection (PAD) algorithms across four diverse ID card datasets, where two datasets contain bona fide, i.e. pristine, images and two contain printed mock ID cards. Our results suggest that quality assessment based on some OFIQ measures can significantly improve PAD performance.

Problem

• Image Quality Assessment
• Identity Cards
• Presentation Attack Detection
• Remote Verification
• OFIQ

Innovation

• Image Quality Assessment
• Open Face Image Quality (OFIQ)
• Presentation Attack Detection (PAD)
• Perspective Normalization
• Foreground Masking