Abstract
Extracting effective minutiae is difficult for high-resolution palmprint, because of the strong influence from principal lines, creases, and other noises. In this paper, a novel minutiae detection and reliability measurement method is proposed for high-resolution palmprint minutiae extraction. Firstly, we propose the Gabor Amplitude-Phase model for palmprint representation, which contains sufficient palmprint information and consists of the phase field and amplitude field. Because of the explicit meanings of minutiae in phase field, a minutiae descriptor is constructed to detect them directly. Also, to measure minutiae reliability and remove the unreliable ones, the Gabor Amplitude-Phase feature vector is designed. It can be used for describing the local area of a minutia redundantly. Then, the Adaboost algorithm is introduced in model training to select best features and corresponding weak classifiers for minutiae authenticity discriminant. Finally, the response value of weighted linear combination of selected weak classifiers is used for minutiae reliability measurement and unreliable ones removal. According to our analysis, the selected features are meaningful and useful for describing the minutiae area and measuring their reliability. Experimental results show that our proposed method is effective for minutiae extraction and can improve the matching performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Kong A, Zhang D, Kamel M. A survey of palmprint recognition. Pattern Recognit, 2009, 42: 1408–1418
Kumar A, Shekhar S. Palmprint recognition using rank level fusion. In: Proceedings of IEEE 17th International Conference on Image Processing, Hong Kong, 2010. 3121–3124
Guo Z, Zhang D, Zhang L, et al. Palmprint verification using binary orientation co-occurrence vector. Pattern Recognit Lett, 2009, 30: 1219–1227
Jain A K, Feng J. Latent palmprint matching. IEEE Trans Pattern Anal Mach Intell, 2009, 31: 1032–1047
Dai J, Zhou J. Multifeature-based high-resolution palmprint recognition. IEEE Trans Pattern Anal Mach Intell, 2011, 33: 945–975
Liu E, Jain A K, Tian J. A coarse to fine minutiae-based latent palmprint matching. IEEE Trans Pattern Anal Mach Intell, 2013, 35: 2307–2322
Raffaele C, Matteo F, Dario M. A fast and accurate palmprint recognition system based on minutiae. IEEE Trans Syst Man Cybern Part B-Cybern, 2012, 42: 956–962
Maltoni D, Maio D, Jain A K, et al. Handbook of Fingerprint Recognition. 2nd ed. Berlin: Springer, 2009. 163–166
Chen T P, Jiang X, Yau W Y. Fingerprint image quality analysis. In: Proceedings of IEEE 11th International Conference on Image Processing, Singapore, 2004. 1253–1256
Fronthaler H, Kollreider K, Bigun J, et al. Fingerprint image-quality estimation and its application to multi-algorithm verification. IEEE Trans Inf Forensic Secur, 2008, 3: 331–338
Aguilar J F, Chen Y, Ortega G J, et al. Incorporating image quality in multi-algorithm fingerprint verification. In: Proceedings of the 2006 International Conference on Advances in Biometrics. Berlin: Springer, 2005. 213–220
Lim E, Toh K A, Suganthan P N, et al. Fingerprint image quality analysis. In: Proceedings of IEEE 11th International Conference on Image Processing, Singapore, 2004. 1241–1244
Chen Y, Dass S C, Jain A K. Fingerprint quality indices for predicting authentication performance. In: Proceedings of the 5th International Conference on Audio-and Video-Based Biometric Person Authentication. Berlin: Springer, 2005. 160–170
Hong L, Wan Y, Jain A K. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell, 1998, 20: 777–789
Gao X, Chen X, Cao J, et al. A novel method Of fingerprint minutiae extraction based on Gabor phase. In: Proceedings of IEEE 17th International Conference on Image Processing, Hong Kong, 2010. 811–814
Bernard S, Boujemaa N, Vitale D, et al. Fingerprint segmentation using the phase of multiscale gabor wavelets. In: Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, 2002. 23–25
Ratha N K, Chen S Y, Jain A K. Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognit, 1995, 28: 1657–1672
Ji L, Yi Z, Shang L, et al. Binary fingerprint image thinning using template-based pcnns. IEEE Trans Syst Man Cybern Part B-Cybern, 2007, 37: 1407–1413
Liu C, Cao J, Gao X, et al. A novel fingerprint matching algorithm using Minutiae Phase Difference Feature. In: Proceedings of IEEE 18th International Conference on Image Processing, Brussels, 2011. 3201–3204
Crow F C. Summed-area tables for texture mapping. In: Proceedings of the 11th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 1984. 207–212
Freund Y, Schapire R E. A decision-theoretic generation of online learning and an application to boosting. J Comput Syst Sci, 1997, 55: 119–139
Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat, 2000, 28: 337–407
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, 2001. 511–518
Viola P, Jones M, Snow D. Detecting pedestrians using patterns of motion and appearance. Int J Comput Vis, 2005, 63: 153–161
Fawcett T. Roc Graphs: Notes and Practical Considerations for Data Mining Researchers. HPL-2003-4. 2003
Bian J. A fast palmprint matching algorithm based on lattice technology. Dissertation for Master Degree. Beijing: Peking University, 2013
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Feng, J., Liu, C., Wang, H. et al. High-resolution palmprint minutiae extraction based on Gabor feature. Sci. China Inf. Sci. 57, 1–15 (2014). https://doi.org/10.1007/s11432-014-5125-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-014-5125-5