Abstract
We propose to combine support vector machine (SVM) models learned from different visual features for efficiently classifying fingerprint images. Real datasets of fingerprint images are collected from students at the Can Tho University. The SVM algorithm learns classification models from the handcrafted features such as the scale-invariant feature transform (SIFT) and the bag-of-words (BoW) model, the histogram of oriented gradients (HoG), the deep learning of invariant features Xception, extracted from fingerprint images. Followed which, we propose to train a neural network for combining SVM models trained on these different visual features, making improvements of the fingerprint image classification. The empirical test results show that combining SVM models is more accurate than SVM models trained on any single visual feature type. Combining SVM-SIFT-BoW, SVM-HoG, SVM-Xception improves 11.17%, 14.07%, 10.83% classification accuracy of SVM-SIFT-BoW, SVM-HoG and SVM-Xception, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org. https://www.tensorflow.org/
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_40
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(27), 1–27 (2011)
Chollet, F., et al.: Keras (2015). https://keras.io
Chollet, F.: Xception: deep learning with depthwise separable convolutions. CoRR abs/1610.02357 (2016)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods. Cambridge University Press, New York (2000)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE Computer Society (2005)
Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_6
Do, T.-N., Lenca, P., Lallich, S.: Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees. Vietnam J. Comput. Sci. 2(1), 3–12 (2014). https://doi.org/10.1007/s40595-014-0024-7
Do, T.-N., Pham, T.-P., Pham, N.-K., Nguyen, H.-H., Tabia, K., Benferhat, S.: Stacking of SVMs for classifying intangible cultural heritage images. In: Le Thi, H.A., Le, H.M., Pham Dinh, T., Nguyen, N.T. (eds.) ICCSAMA 2019. AISC, vol. 1121, pp. 186–196. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38364-0_17
Engelsma, J.J., Cao, K., Jain, A.K.: Fingerprints: Fixed length representation via deep networks and domain knowledge. CoRR abs/1904.01099 (2019)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9(4), 1871–1874 (2008)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)
ItSeez: Open source computer vision library (2015). https://github.com/itseez/opencv
Jain, A.K., Feng, J., Nandakumar, K.: Fingerprint matching. IEEE Comput. 43(2), 36–44 (2010)
Jain, A.K., Nandakumar, K., Ross, A.: 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn. Lett. 79, 80–105 (2016)
Kreßel, U.H.G.: Pairwise classification and support vector machines. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods, pp. 255–268. MIT Press, Cambridge (1999)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Li, F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 20–26 June 2005, San Diego, CA, USA, pp. 524–531 (2005)
Lin, C.: A practical guide to support vector classification (2003)
Lowe, D.: Object recognition from local scale invariant features. In: Proceedings of the 7th International Conference on Computer Vision, pp. 1150–1157 (1999)
Lowe, D.: Distinctive image features from scale invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, vol. 1, pp. 281–297 (1967)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, London (2009). https://doi.org/10.1007/978-1-84882-254-2
Minaee, S., Azimi, E., Abdolrashidi, A.: FingerNet: Pushing the limits of fingerprint recognition using convolutional neural network. CoRR abs/1907.12956 (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Sharma, V., Gool, L.V.: Image-level classification in hyperspectral images using feature descriptors, with application to face recognition. CoRR abs/1605.03428 (2016)
Shrein, J.M.: Fingerprint classification using convolutional neural networks and ridge orientation images. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (November 2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: 9th IEEE International Conference on Computer Vision, ICCV 2003, Nice, France, 14–17 October 2003, pp. 1470–1477 (2003)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-2440-0
Wolpert, D.: Stacked generalization. Neural Netw. 5, 241–259 (1992)
Acknowledgments
This work has received support from the College of Information Technology, Can Tho University. The authors would like to thank very much the Big Data and Mobile Computing Laboratory.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pham, TP., Tran-Nguyen, MT., Tran, MT., Do, TN. (2020). Combining Support Vector Machines for Classifying Fingerprint Images. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. FDSE 2020. Lecture Notes in Computer Science(), vol 12466. Springer, Cham. https://doi.org/10.1007/978-3-030-63924-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-63924-2_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63923-5
Online ISBN: 978-3-030-63924-2
eBook Packages: Computer ScienceComputer Science (R0)