An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

TL;DR

This paper reviews spectral imaging techniques in biometric face recognition, discussing challenges, recent advancements, and the potential of deep learning to improve robustness against common issues like illumination and pose variations.

Contribution

It provides a comprehensive overview of spectral imaging in face recognition, highlighting current challenges, available datasets, and recent deep learning-based advancements in the field.

Findings

Spectral imaging enhances face recognition robustness.

Deep learning methods show promising results.

Public datasets facilitate reproducible research.

Abstract

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images.