Abstract
AbstractBiometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment ofbiometric qualityan important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.
Highlights
Biometrics, as an integral component in identification science, is being utilized in large-scale biometrics deployments such as the US Visitor and Immigration Status Indicator Technology (VISIT), UK Iris Recognition Immigration System (IRIS) project, UAE iris-based airport security system, and India’s Aadhaar project
5 Conclusions Quality assessment of biometric samples is an important challenge for the biometrics research community
A clear distinction is made between the image quality and biometric quality of a biometric sample to capture modality-specific intuitions of quality assessment
Summary
Biometrics, as an integral component in identification science, is being utilized in large-scale biometrics deployments such as the US Visitor and Immigration Status Indicator Technology (VISIT), UK Iris Recognition Immigration System (IRIS) project, UAE iris-based airport security system, and India’s Aadhaar project. More research focus must be directed towards this problem, since it has been observed in several empirical studies including the findings of biometric grand challenges that the covariates of face recognition (pose, illumination, expression, noise) affect the performance across different types of features or systems. The NIST Multiple-Biometric Evaluation (MBE) [86] presents six state-of-the-art commercial face recognition systems on various demographic and covariate challenges which indicate that the performance of all algorithms is affected by various factors such as gender, age, and ethnicity, apart from known covariates of pose, illumination, and expression It follows that a quantitative measure of quality of an input face image that provides an estimate of matching performance is critical. A quality metric that entails a greater insight of the usefulness of the biometric sample in consideration can improve the performance of these systems by providing more discernible quality cohorts
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