Trustworthy Compression? Impact of AI-based Codecs on Biometrics for Law Enforcement

TL;DR

This study evaluates how AI-based image compression affects biometric recognition, revealing that iris recognition is sensitive to compression while fingerprint recognition remains robust, with implications for law enforcement use.

Contribution

It provides an empirical analysis of AI compression effects on biometric images, highlighting the varying robustness across different biometric modalities.

Findings

Iris recognition performance declines with strong AI compression.

Fingerprint recognition remains robust under AI compression.

Strong compression affects soft-biometric details like fabrics and tattoos.

Abstract

Image-based biometrics can aid law enforcement in various aspects, for example in iris, fingerprint and soft-biometric recognition. A critical precondition for recognition is the availability of sufficient biometric information in images. It is visually apparent that strong JPEG compression removes such details. However, latest AI-based image compression seemingly preserves many image details even for very strong compression factors. Yet, these perceived details are not necessarily grounded in measurements, which raises the question whether these images can still be used for biometric recognition. In this work, we investigate how AI compression impacts iris, fingerprint and soft-biometric (fabrics and tattoo) images. We also investigate the recognition performance for iris and fingerprint images after AI compression. It turns out that iris recognition can be strongly affected, while fingerprint recognition is quite robust. The loss of detail is qualitatively best seen in fabrics and tattoos images. Overall, our results show that AI-compression still permits many biometric tasks, but attention to strong compression factors in sensitive tasks is advisable.