Fingerprint classification based on Adaboost learning from singularity features

Fingerprint classification is an important indexing scheme to narrow down the search of fingerprint database for efficient large-scale identification. It is still a challenging problem due to the intrinsic class ambiguity and the difficulty for poor quality fingerprints. In this paper, we presents a fingerprint classification algorithm that uses Adaboost learning method to model multiple types of singularity features. Firstly, complex filters are used to detect the singularities. For powerful representation, we compute the complex filter responses of the detected singularities at multiple scales and a feature vector is constructed for each scale that consists of the relative position and direction and the certainties of the singularities. Adaboost learning method is then applied on decision trees to design a classifier for fingerprint classification. Finally, fingerprint class is determined by the ensemble of the classification results at multiple scales. The experimental results and comparisons on NIST-4 database have shown the effectiveness and superiority of the fingerprint classification algorithm.

[1]  Ugur Halici,et al.  Fingerprint classification through self-organizing feature maps modified to treat uncertainties , 1996, Proc. IEEE.

[2]  Hai Jun Yang,et al.  Boosted decision trees, a powerful event classifier , 2006 .

[3]  J. Bigun Pattern recognition in images by symmetries and coordinate transformations , 1997 .

[4]  Xudong Jiang,et al.  Efficient fingerprint search based on database clustering , 2007, Pattern Recognit..

[5]  Xudong Jiang,et al.  Fingerprint Retrieval by Complex Filter Responses , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  Haesun Park,et al.  Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis , 2005, Pattern Recognit..

[7]  Khaled Ahmed Nagaty Fingerprints classification using artificial neural networks: a combined structural and statistical approach , 2001, Neural Networks.

[8]  Dario Maio,et al.  A structural approach to fingerprint classification , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Anil K. Jain,et al.  A Real-Time Matching System for Large Fingerprint Databases , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Sabih H. Gerez,et al.  Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Xudong Jiang,et al.  Fingerprint Reference-Point Detection , 2005, EURASIP J. Adv. Signal Process..

[12]  Adnan Amin,et al.  Fingerprint classification: a review , 2004, Pattern Analysis and Applications.

[13]  Kuo-Chin Fan,et al.  A new model for fingerprint classification by ridge distribution sequences , 2002, Pattern Recognit..

[14]  Yoram Singer,et al.  Logistic Regression, AdaBoost and Bregman Distances , 2000, Machine Learning.

[15]  Yuan Yao,et al.  Fingerprint Classification with Combinations of Support Vector Machines , 2001, AVBPA.

[16]  Marios S. Pattichis,et al.  Fingerprint classification using an AM-FM model , 2001, IEEE Trans. Image Process..

[17]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[18]  Gian Luca Marcialis,et al.  Fingerprint Classification by Combination of Flat and Structural Approaches , 2001, AVBPA.

[19]  Loris Nanni,et al.  A two-stage fingerprint classification system , 2003, WBMA '03.

[20]  Edward Richard Henry,et al.  Classification and uses of finger prints , 1928 .

[21]  Kai Huang,et al.  Fingerprint Classification Based on Extraction and Analysis of Singularities and Pseudoridges , 2001, VIP.

[22]  Anil K. Jain,et al.  Hierarchical kernel fitting for fingerprint classification and alignment , 2002, Object recognition supported by user interaction for service robots.

[23]  Bir Bhanu,et al.  Fingerprint classification based on learned features , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[24]  Yuan Yao,et al.  Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines , 2003, Pattern Recognit..

[25]  Anil K. Jain,et al.  Fingerprint classification , 1996, Pattern Recognit..

[26]  Anil K. Jain,et al.  A Multichannel Approach to Fingerprint Classification , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Xudong Jiang On orientation and anisotropy estimation for online fingerprint authentication , 2005, IEEE Transactions on Signal Processing.

[28]  Sung-Bae Cho,et al.  Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers , 2008, Pattern Recognit..

[29]  Josef Bigün,et al.  Localization of corresponding points in fingerprints by complex filtering , 2003, Pattern Recognit. Lett..

[30]  Andrew W. Senior,et al.  A Combination Fingerprint Classifier , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Alessandra Lumini,et al.  Fingerprint Classification by Directional Image Partitioning , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Craig I. Watson,et al.  PCASYS- A Pattern-Level Classification Automation System for Fingerprints | NIST , 1995 .

[33]  Xiaoou Tang,et al.  Combining exclusive and continuous fingerprint classification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[34]  Jun Li,et al.  Combining singular points and orientation image information for fingerprint classification , 2008, Pattern Recognit..

[35]  Sabih H. Gerez,et al.  Segmentation of Fingerprint Images , 2001 .

[36]  Anil K. Jain,et al.  Fingerprint Image Enhancement: Algorithm and Performance Evaluation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Boris Bratnina AKADEMSKO PISANJE U DRUŠTVENIM NAUKAMA , 2011 .