Quality assessment of image-based biometric information

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.

[1]  M. S. Sutaone,et al.  Iris Image Quality Assessment for Biometric Application , 2012 .

[2]  Anil K. Jain,et al.  Performance evaluation of fingerprint verification systems , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Charles L. Wilson,et al.  A novel approach to fingerprint image quality , 2005, IEEE International Conference on Image Processing 2005.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  S. Gabarda,et al.  Blind image quality assessment through anisotropy. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[7]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[8]  Qian He,et al.  A hierarchical model for the evaluation of biometric sample quality , 2008, 2008 19th International Conference on Pattern Recognition.

[9]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.

[10]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[11]  Albert Ali Salah,et al.  Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms , 2009, IEEE Transactions on Information Forensics and Security.

[12]  A. Martínez,et al.  The AR face databasae , 1998 .

[13]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[14]  Alberto Del Bimbo,et al.  A Set of Selected SIFT Features for 3D Facial Expression Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[15]  Josef Kittler,et al.  Quality-Based Score Normalization With Device Qualitative Information for Multimodal Biometric Fusion , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Xudong Jiang,et al.  Fingerprint quality and validity analysis , 2002, Proceedings. International Conference on Image Processing.

[17]  B. Dorizzi,et al.  A new probabilistic Iris Quality Measure for comprehensive noise detection , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[18]  Anil K. Jain,et al.  FVC2002: Second Fingerprint Verification Competition , 2002, Object recognition supported by user interaction for service robots.

[19]  Gilbert Saporta,et al.  Probabilités, Analyse des données et statistique , 1991 .

[20]  Anil K. Jain,et al.  Fingerprint Quality Indices for Predicting Authentication Performance , 2005, AVBPA.

[21]  Christophe Rosenberger,et al.  Palm Vein Verification System Based on SIFT Matching , 2009, ICB.

[22]  Natalia A. Schmid,et al.  Estimating and Fusing Quality Factors for Iris Biometric Images , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[24]  Stan Z. Li,et al.  Standardization of Face Image Sample Quality , 2007, ICB.

[25]  Alex ChiChung Kot,et al.  Quality assessment of finger-vein image , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[26]  Elham Tabassi,et al.  Performance of Biometric Quality Measures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Jitendra Malik,et al.  Matching Shapes , 2001, ICCV.

[29]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Christophe Charrier,et al.  A DCT Statistics-Based Blind Image Quality Index , 2010, IEEE Signal Processing Letters.

[31]  LinLin Shen,et al.  Quality Measures of Fingerprint Images , 2001, AVBPA.

[32]  Josef Kittler,et al.  A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms , 2010, Pattern Recognit..

[33]  Enrico Grosso,et al.  Face Identification by SIFT-based Complete Graph Topology , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[34]  Yunhong Wang,et al.  Asymmetry-Based Quality Assessment of Face Images , 2009, ISVC.

[35]  Julian Fiérrez,et al.  A Comparative Study of Fingerprint Image-Quality Estimation Methods , 2007, IEEE Transactions on Information Forensics and Security.

[36]  Kenneth Ko,et al.  User's Guide to NIST Biometric Image Software (NBIS) , 2007 .

[37]  Javier Ortega-Garcia,et al.  Iris recognition based on SIFT features , 2004, 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS).

[38]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

[39]  Christophe Charrier,et al.  Fast Pixel Classification by SVM Using Vector Quantization, Tabu Search and Hybrid Color Space , 2005, CAIP.

[40]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[41]  Sharath Pankanti,et al.  Fingerprint verification using SIFT features , 2008, SPIE Defense + Commercial Sensing.

[42]  Stan Z. Li,et al.  Face Image Quality Evaluation for ISO/IEC Standards 19794-5 and 29794-5 , 2009, ICB.