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Auflistung nach Schlagwort "Biometric sample quality"

1 - 2 von 2
  • Konferenzbeitrag
    Unified Face Image Quality Score based on ISO/IEC Quality Components
    (BIOSIG 2023, 2023) Praveen Kumar Chandaliya, Kiran Raja
    Face image quality assessment is crucial in the face enrolment process to obtain high-quality face images in the reference database. Neglecting quality control will adversely impact the accuracy and efficiency of face recognition systems, resulting in an image captured with poor perceptual quality. In this work, we present a holistic combination of $21$ component quality measures proposed in ``ISO/IEC CD 29794-5" and identify the varying nature of different measures across different datasets. The variance is seen across both capture-related and subject-related measures, which can be tedious for validating each component metric by a human observer when judging the quality of the enrolment image. Motivated by this observation, we propose an efficient method of combining quality components into one unified score using a simple supervised learning approach. The proposed approach for predicting face recognition performance based on the obtained unified face image quality assessment (FIQA) score was comprehensively evaluated using three datasets representing diverse quality factors. We extensively evaluate the proposed approach using the Error-vs-Discard Characteristic (EDC) and show its applicability using five different FRS. The evaluation indicates promising results of the proposed approach combining multiple component scores into a unified score for broader application in face image enrolment in FRS.
  • Konferenzbeitrag
    Utility prediction performance of finger image quality assessment software
    (BIOSIG 2023, 2023) Olaf Henniger
    A biometric sample is the more utile for biometric recognition the greater the distance between the sample-specific non-mated and mated comparison score distributions. Finger image quality scores turn out to be only weakly correlated with the observed utility. This is worth investigating because finger image quality assessment software is widely used to predict the biometric utility of finger images in many public-sector applications. This paper shows that a weak correlation between predicted and observed utility does not matter if the quality scores are used to decide whether to discard or retain biometric samples for further processing. The important point is that useful samples are not mistakenly discarded or less useful samples are not mistakenly retained. This can be measured by quality-assessment false positive and false negative rates. In cost-benefit analyses, these metrics can be used to chose suitable quality-score thresholds for the use cases at hand.