Fingerprint Classi cation in .NET

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14.6 Fingerprint Classi cation
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Figure 14.14. The main feature extraction steps of the system proposed in reference 77.
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Jain et al. [83] adopt a two-stage classi cation strategy: A K-nearest neighbor classi er is used to nd the two most likely classes from a FingerCode feature vector; then a set of ten neural networks are trained to distinguish between each possible pair of classes. Yao et al. [84] use a combination of SVMs trained by two kind of features: the FingerCode representation of the ngerprint [85] and a distributed vectorial representation of the relational graph associated with the ngerprint obtained by Recursive Neural Networks. Cappelli et al. [86] improve the results obtained by [83] using a two-stage sequential architecture based on an MKL classi er to select the two-most-likely classes and a set of 10 SPD classi ers to discriminate between selected pair of classes. In references 67, 92, and 87, some classi ers based on the MKL transform are combined. In Table 14.7, some comparisons among different ngerprint classi cation methods are reported for the NIST DB4 [88]: Most of them were obtained by using the rst half of the database for training and the second half for testing. Furthermore, since DB4 contains an equal number of ngerprints for each class, some authors prefer to
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Table 14.7. Comparison Among Different Classi cation Approaches on DB4a Five Classes Method Candela et al. [73] Senior [71] Jain et al. [83] Cappelli et al. [89] Marcialis et al. [80] Yao et al. [82] Senior [81] Yao et al. [84] Cappelli and Maio [67] Tan et al. [79] Category Neural network Ridge line shape Combined Other Combined Combined Combined Combined Combined Other Equal 10.0% 7.9% 12.1% 10.7% 10% 7.0% 8.4% Weighted 7.0% 6.5% 9.6% 9.0% 8.1% 5.9% 8.3% Four Classes Equal 11.4% 5.2% 5.5% 6.9% 5.3% 4.7% 6.7% Weighted 6.1% 8.4% 5.1% 5.4% 6%
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a Classi cation error is reported for the ve-class and four-class problems. In the four-class problem, Arch
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and Tented-Arch classes are here fused into a single class.
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14
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Learning in Fingerprints
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weight the results according to the natural class distribution. All the results are reported at 0% rejection rate, with the exception of the approaches based on FingerCode feature vectors (Jain et al. [83], Marcialis et al. [80], Yao et al. [82], Yao et al. [84]), where 1.8% ngerprints are discarded during the feature extraction stage.
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CONCLUSION
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In this chapter the main learning-based approaches to ngerprint acquisition, processing, recognition, and classi cation have been discussed. It is not surprising that tasks where the application of learning seems to provide maximum bene t are those that are critical for humans (e.g., quality check, liveness detection, continuous and exclusive classi cation, etc.). Combination of classi ers (both at feature and at score level) and automatic selection of redundant features proved to be very effective to improve the accuracy of ngerprint veri cation; SVM and other robust classi cation techniques were successfully applied to ngerprint veri cation formulated as a two class problem. The two main factors limiting the performance of learning based techniques are still the availability of reliable large-enough training data set and the lack of ef ciency of some techniques such as the exhaustive classi cation of small portions of the image to detect minutiae. The authors believe that important advances could arise by the availability of large sets of ad hoc-created synthetic ngerprints [90] and the implementation of boosted cascade of weak classi ers (such as in reference 3) that allows us to solve in real time and with good accuracy the face detection problem.
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ACKNOWLEDGMENTS
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The authors want to thank F. Roli, G. Marcialis, and P. Coli for sharing the vitality data set used in this chapter.
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REFERENCES
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1. D.Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, in Handbook of Fingerprint Recognition, Springer, Berlin, 2003. 2. R. Duda, P. Hart, and D. Stork, Pattern Classi cation, John Wiley & Sons, New York, 2000. 3. P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, 2001, pp. 511 518. 4. L. I. Kuncheva and C. J. Whitaker, Measures of diversity in classi er ensembles and their relationship with the ensemble accuracy, Mach. Learning 51:181 207, 2003. 5. N. J. Nilsson, Introduction to Machine Learning, Stanford University, Palo Alto, CA, 1997. 6. F. Alonso-Fernandez, J. Fierrez-Aguilar, and J. Ortega-Garcia, A review of schemes for ngerprint image quality computation, in COST 275 Biometrics Based Recognition of People over the Internet, October 2005. 7. E. Tabassi, C.Wilson, and C.Watson, Fingerprint image quality, NIST Research Report NISTIR 7151, August 2004.
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