Abstract
This paper focuses on automatic pattern-based extracting of biometric features where finger-knuckle images are analyzed. Knuckle images are captured by digital camera, and then by the image processing techniques the most relevant features (patterns) are discovered and extracted. Knuckle-based images were filtered by the Hessian filters. It enabled to enhance image regions with image ridges. In the next stage similarity of images were computed by the Normalized Cross-Correlation algorithm. Ultimately, similarities were classified by the k-NN classifier. The discovered features belong to so-called human physical features, which involves innate human characteristics. Physical biometric features can often be gathered with specialized hardware, needing only software for analysis. That capacity makes such biometrics simpler.
We conducted a variety of experiments and showed advantages and disadvantages of the approaches with promising results.
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References
Ferrer, M.A., Travieso, C.M., Alonso, J.B.: Using hand Knuckle texture for biometric identifications. IEEE Aerosp. Electron. Syst. Mag. 21(6), 23–27 (2006)
Iwahori, Y., Hattori, A., Adachi, Y., Bhuyan, M.K., Woodham, R.J., Kasugai, K.: Automatic detection of polyp using Hessian Filter and HOG features. Procedia Comput. Sci. 60(1), 730–739 (2015)
Jin, J., Yang, L., Zhang, X., Ding, M.: Vascular tree segmentation in medical images using Hessian-based multiscale filtering and level set method. Comput. Math. Methods Med. 2013, 502013 (2013)
Kasprowski, P.: The impact of temporal proximity between samples on eye movement biometric identification. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 77–87. Springer, Heidelberg (2013)
Koprowski, R., Teper, S.J., Weglarz, B., Wylegała, E., Krejca, M., Wróbel, Z.: Fully automatic algorithm for the analysis of vessels in the angiographic image of the eye fundus. Biomed. Eng. Online 11 (2012)
Koprowski, R., Wilczynski, S., Wrobel, Z., Kasperczyk, S., Blonska-Fajfrowska, B.: Automatic method for the dermatological diagnosis of selected hand skin features in hyperspectral imaging. Biomed. Eng. Online 13 (2014)
Kumar, A., Ravikanth, C.: Personal authentication using finger Knuckle surface. IEEE Trans. Inf. Forensics Secur. 4(1), 98–110 (2009)
Kumar, A., Wang, B.: Recovering and matching minutiae patterns from finger Knuckle images. Pattern Recogn. Lett. 68, 361–367 (2015)
Kumar, A., Zhou, Y.: Human identification using Knuckle codes. In: Proceedings BTAS 2009, pp. 98–109 (2009)
Lewis, J.P.: Fast normalized cross-correlation. Vis. Interface 10(1), 120–123 (1995)
Li, B., Wang, K., Zhang, D.: On-line signature verification based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS(LNAI, LNBI), vol. 3072, pp. 540–546. Springer, Heidelberg (2004)
Morales, A., Travieso, C.M., Ferrer, M.A., Alonso, J.B.: Improved finger-Knuckle-print authentication based on orientation enhancement. Electron. Lett. 47(6), 380–382 (2011)
Nakhmani, A., Tannenbaum, A.: A new distance measure based on generalized Image Normalized Cross-Correlation for robust video tracking and image recognition. Pattern Recogn. Lett. 34(3), 315–321 (2013)
Nitsch, J., Klein, J., Miller, D., Sure, U., Hahn, K.H.: Automatic segmentation of the Cerebral Falx and adjacent Gyri in 2D ultrasound images. Bildverarbeitung für die Medizin 2015: Algorithmen - Systeme - Anwendungen, pp. 287–292. Springer, Heidelberg (2015)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). http://dx.doi.org/10.1109/tsmc.1979.4310076
Pavlidis, T.: A thinning algorithm for discrete binary images. Comput. Graph. Image Process. 13(2), 142–157 (1980)
Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 377–388. Springer, Heidelberg (2014)
Porwik, P., Doroz, R., Wrobel, K.: A new signature similarity measure. In: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings, pp. 1022–1027 (2009)
Wei, S.D., Lai, S.H.: Fast template matching based on normalized cross correlation with adaptive multilevel winner update. IEEE Trans. Image Process. 17(11), 2227–2235 (2008)
Di Stefano, L., Mattoccia, S., Tombari, F.: An algorithm for efficient and exhaustive template matching. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 408–415. Springer, Heidelberg (2004)
Usha, K., Ezhilarasan, M.: Finger Knuckle biometrics - a review. Comput. Electr. Eng. 45, 249–259 (2015)
Woodard, D.L., Flynn, P.J.: Finger surface as a biometric identifier. Comput. Vis. Image Underst. 100(3), 357–384 (2005)
Xiong, M., Yang, W., Sun, C.: Finger-Knuckle-print recognition using LGBP. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part II. LNCS, vol. 6676, pp. 270–277. Springer, Heidelberg (2011)
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Doroz, R. et al. (2016). A New Personal Verification Technique Using Finger-Knuckle Imaging. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_49
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DOI: https://doi.org/10.1007/978-3-319-45246-3_49
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