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Multi-modality quality assessment for unconstrained biometric samples

Xinwei Liu 1
1 Equipe Monétique & Biométrie - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : The aim of this research is to investigate multi-modality biometric image qualityassessment methods for unconstrained samples. Studies of biometrics noted thesignificance of sample quality for a recognition system or a comparison algorithmbecause the performance of the biometric system depends mainly on the qualityof the sample images. The need to assess the quality of multi-modality biometricsamples is increased with the requirement of a high accuracy multi-modalitybiometric systems.Following an introduction and background in biometrics and biometric samplequality, we introduce the concept of biometric sample quality assessment for multiplemodalities. Recently established ISO/IEC quality standards for fingerprint,iris, and face are presented. In addition, sample quality assessment approacheswhich are designed specific for contact-based and contactless fingerprint, nearinfrared-based iris and visible wavelength iris, as well as face are surveyed. Followingthe survey, approaches for the performance evaluation of biometric samplequality assessment methods are also investigated.Based on the knowledge gathered from the biometric sample quality assessmentchallenges, we propose a common framework for the assessment of multi-modalitybiometric image quality. We review the previous classification of image-basedquality attributes for a single biometric modality and investigate what are the commonimage-based attributes for multi-modality. Then we select and re-define themost important image-based quality attributes for the common framework. In order to link these quality attributes to the real biometric samples, we develop anew multi-modality biometric image quality database which has both high qualitysample images and degraded images for contactless fingerprint, visible wavelengthiris, and face modalities. The degradation types are based on the selected commonimage-based quality attributes. Another important aspect in the proposed commonframework is the image quality metrics and their applications in biometrics. Wefirst introduce and classify the existing image quality metrics and then conducteda brief survey of no-reference image quality metrics, which can be applied to biometricsample quality assessment. Plus, we investigate how no-reference imagequality metrics have been used for the quality assessment for fingerprint, iris, andface biometric modalities.The experiments for the performance evaluation of no-reference image qualitymetrics for visible wavelength face and iris modalities are conducted. The experimentalresults indicate that there are several no-reference image quality metricsthat can assess the quality of both iris and face biometric samples. Lastly, we optimizethe best metric by re-training it. The re-trained image quality metric canprovide better recognition performance than the original. Through the work carriedout in this thesis we have shown the applicability of no-reference image qualitymetrics for the assessment of unconstrained multi-modality biometric samples.
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Submitted on : Friday, September 27, 2019 - 3:54:07 PM
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  • HAL Id : tel-02299278, version 1


Xinwei Liu. Multi-modality quality assessment for unconstrained biometric samples. Image Processing [eess.IV]. Normandie Université; Gjøvik University College (Norvège), 2018. English. ⟨NNT : 2018NORMC284⟩. ⟨tel-02299278⟩



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