A novel approach to fingerprint image quality

Tabassi, Elham; Wilson, Charles L.

doi:10.1109/icip.2005.1529985

Cited by 82 publications

(60 citation statements)

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“…Nowadays, several studies have been done in the literature to evaluate biometric systems. It is generally realised within three aspects as illustrated in Figure 1: Figure 1 Evaluation aspects of biometric systems 1 Data quality: Measures the quality of the biometric raw data (Tabassi and Wilson, 2005). Using quality information, the bad-quality samples can be removed during enrolment or rejected during verification.…”

Section: • Behaviouralmentioning

confidence: 99%

Evaluation of biometric systems: a study of users' acceptance and satisfaction

El-Abed¹,

Giot²,

Hemery³

et al. 2012

IJBM

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This paper presents a modality-independent evaluation methodology to study users' acceptance and satisfaction of biometric systems. It uses a survey questionnaire for data collection, and some data-mining tools for their analysis. We have applied it on two biometric systems developed in our research laboratory. The results from this survey show the necessity of taking users' point of view when designing and evaluating biometric systems. A panel of 100 volunteers was more satisfied from the keystroke system than the face one. Users surprisingly considered that its perceived performance was also better, even if the used face system has a better performance with an EER of 8.76% than the keystroke one with an EER of 17.51%. The robustness of a system against attacks, computation time required during the verification phase and its easiness to use have been identified as important factors influencing their opinions regarding the tested systems.

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Section: • Behaviouralmentioning

confidence: 99%

Evaluation of biometric systems: a study of users' acceptance and satisfaction

El-Abed¹,

Giot²,

Hemery³

et al. 2012

IJBM

View full text Add to dashboard Cite

show abstract

“…Methods based on global features (Chen, et al 2005;Lim, et al 2004) analyze the overall image and compute a global quality based on the features extracted. The method that uses classifiers (Tabassi, et al,2004, Tabassi, et al 2005) defines the quality measure as a degree of separation between the match and non-match distributions of a given fingerprint.…”

Section: Feature Analysis For Fingerprint Quality Estimationmentioning

confidence: 99%

“…The NFIS method (Tabassi, E.,2004;Tabassi, E.,2005) is the most widely used method and typical classifier-based methods for fingerprint quality estimation. The method proposed the assumption that fingerprint quality is a predictor of matcher performance.…”

Section: Quality Benchmarkmentioning

confidence: 99%

Fingerprint Quality Analysis and Estimation Approach for Fingerprint Matching

Xie¹,

Yang²,

Park³

et al. 2011

State of the Art in Biometrics

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“…Many quality algorithms have been developed mainly for the fingerprint modality [9], [10], face [5], [11], iris [4], voice [12] and signature signals [13]. These works have demonstrated that the performance of biometric systems is heavily affected by the quality of the acquired biometric data.…”

Section: Introductionmentioning

confidence: 99%

“…These works have demonstrated that the performance of biometric systems is heavily affected by the quality of the acquired biometric data. Tabassi et al present in [9] a method based on the measurement of the matching scores to assess fingerprint quality. The proposed method uses a black box composed of two modules, feature extraction and neural network, which associates the image quality into five classes (excellent, very good, good, fair and poor).…”

Section: Introductionmentioning

confidence: 99%

A SVM-based model for the evaluation of biometric sample quality

El-Abed

Giot

Hemery

et al. 2011

2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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Abstract-One of the main factors affecting the performance of biometric systems is the quality of the acquired samples. Poorquality samples increase the enrollment failure, and decrease the system performance. Therefore, it is important for a biometric system to estimate the quality of the acquired biometric samples. Toward this goal, we present in this paper a multi-class SVMbased method to predict sample quality. The proposed method uses two types of information: the first one is based on the image quality and the second is a pattern-based quality using the SIFT keypoints extracted from the image. For the experiments, we use four large and significant face databases to show the efficiency of the proposed method in predicting the system performance illustrated by the Equal Error Rate (EER).

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A novel approach to fingerprint image quality

Cited by 82 publications

References 1 publication

Evaluation of biometric systems: a study of users' acceptance and satisfaction

Evaluation of biometric systems: a study of users' acceptance and satisfaction

Fingerprint Quality Analysis and Estimation Approach for Fingerprint Matching

A SVM-based model for the evaluation of biometric sample quality

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