Quality assessment of image-based biometric information

被引：7
|
作者：
El-Abed, Mohamad [1 ]
Charrier, Christophe [2 ,3 ,4 ]
Rosenberger, Christophe [2 ,3 ,4 ]
机构：
[1] Rafik Hariri Univ, Meshref, Lebanon
[2] Univ Caen Basse Normandie, UMR GREYC 6072, F-14032 Caen, France
[3] ENSICAEN, UMR GREYC 6072, F-14050 Caen, France
[4] CNRS, UMR GREYC 6072, F-14032 Caen, France
来源：
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING | 2015年
关键词：
Biometrics; No-reference image quality assessment; Scale-invariant feature transformation (SIFT); Support vector machine (SVM); PERFORMANCE EVALUATION;
D O I：
10.1186/s13640-015-0055-8
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The quality of biometric raw data is one of the main factors affecting the overall performance of biometric systems. Poor biometric samples increase the enrollment failure and decrease the system performance. Hence, controlling the quality of the acquired biometric raw data is essential in order to have useful biometric authentication systems. Towards this goal, we present a generic methodology for the quality assessment of image-based biometric modality combining two types of information: 1) image quality and 2) pattern-based quality using the scale-invariant feature transformation (SIFT) descriptor. The associated metric has the advantages of being multimodal (face, fingerprint, and hand veins) and independent from the used authentication system. Six benchmark databases and one biometric verification system are used to illustrate the benefits of the proposed metric. A comparison study with the National Institute of Standards and Technology (NIST) fingerprint image quality (NFIQ) metric proposed by the NIST shows the benefits of the presented metric.
引用
收藏
页码：1 / 15
页数：15
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