Abstract
Nowadays, many smartphones are provided with built-in sensors for the acquisition and the recognition of specific biometric traits of the user. This policy has been adopted since the massive use of such devices brought the user to store sensible data in them as well as effectuate sensitive transactions on-the-move. As a consequence, many biometric systems have been migrated from stand alone to mobile environments. The methodology proposed in the following presents an approach to the iris recognition in visible spectrum. Iris images are first enhanced by a fuzzy color/contrast preserving technique and then passed to a two-tier clustering: the first is based on the linear decomposition of the iris into superpixels; the second one exploits an unsupervised learning network model to built a feature vector of the iris. According to the performance obtained in terms of time and recognition rate, the method is compliant with the needs of real-time and in-movement environments.
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References
Abate, A.F., Nappi, M., Narducci, F., Ricciardi, S.: Fast iris recognition on smartphone by means of spatial histograms. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds.) BIOMET 2014. LNCS, vol. 8897, pp. 66–74. Springer, Heidelberg (2014)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Barra, S., Casanova, A., De Marsico, M., Riccio, D.: Babies: biometric authentication of newborn identities by means of ear signatures. In: Proceedings of the 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), pp. 1–7. IEEE (2014)
Barra, S., Casanova, A., Narducci, F., Ricciardi, S.: Ubiquitous iris recognition by means of mobile devices. Pattern Recogn. Lett. 57, 66–73 (2015). http://www.sciencedirect.com/science/article/pii/S0167865514003286, mobile Iris CHallenge Evaluation part I (MICHE I)
Barra, S., De Marsico, M., Cantoni, V., Riccio, D.: Using mutual information for multi-anchor tracking of human beings. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds.) BIOMET 2014. LNCS, vol. 8897, pp. 28–39. Springer, Heidelberg (2014)
Barra, S., De Marsico, M., Galdi, C., Riccio, D., Wechsler, H.: Fame: face authentication for mobile encounter. In: 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), pp. 1–7. IEEE (2013)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Bullinaria, J.A.: Self organizing maps: fundamentals. In: Introduction to Neural (2004)
Cho, D.H., Park, K.R., Rhee, D.W., Kim, Y., Yang, J.: Pupil and iris localization for iris recognition in mobile phones. In: Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2006), pp. 197–201, June 2006
De Marsico, M., Nappi, M., Daniel, R.: Isis: iris segmentation for identification systems. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2857–2860 (2010)
Haindl, M., Krupika, M.: Unsupervised detection of non-iris occlusions. Pattern Recogn. Lett. 57, 60–65 (2015). http://www.sciencedirect.com/science/article/pii/S0167865515000604, mobile Iris CHallenge Evaluation part I (MICHE I)
Jeong, D.S., Park, H.-A., Park, K.R., Kim, J.: Iris recognition in mobile phone based on adaptive gabor filter. In: Zhang, D., Jain, A.K. (eds.) ICB 2006. LNCS, vol. 3832, pp. 457–463. Springer, Heidelberg (2005). doi:10.1007/11608288_61
Jiang, Y., Zhou, Z.H.: Som ensemble-based image segmentation. Neural Process. Lett. 20(3), 171–178 (2004). doi:10.1007/s11063-004-2022-8
Kiviluoto, K.: Topology preservation in self-organizing maps. Helsinki University of Technology (1995)
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)
Liam, L.W., Chekima, A., Fan, L.C., Dargham, J.A.: Iris recognition using self-organizing neural network. In: Student Conference on Research and Development, SCOReD 2002, pp. 169–172. IEEE (2002)
Marsico, M.D., Galdi, C., Nappi, M., Riccio, D.: Firme: face and iris recognition for mobile engagement. Image Vis. Comput. 32(12), 1161–1172 (2014). http://www.sciencedirect.com/science/article/pii/S0262885614000055
Ong, S., Yeo, N., Lee, K., Venkatesh, Y., Cao, D.: Segmentation of color images using a two-stage self-organizing network. Image Vis. Comput. 20(4), 279–289 (2002). http://www.sciencedirect.com/science/article/pii/S0262885602000215
Proença, H., Alexandre, L.A.: UBIRIS: a noisy iris image database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 970–977. Springer, Heidelberg (2005). doi:10.1007/11553595_119
Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4), 2475–2480 (2010)
Tan, X., Chen, S., Zhou, Z.H., Zhang, F.: Recognizing partially occluded, expression variant faces from single training image per person with som and soft k-NN ensemble. IEEE Trans. Neural Netw. 16(4), 875–886 (2005)
Tan, X., Chen, S., Zhou, Z.H., Zhang, F.: Face recognition from a single image per person: a survey. Pattern Recogn. 39(9), 1725–1745 (2006)
Wang, L., Zhang, Y., Feng, J.: On the euclidean distance of images. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1334–1339 (2005)
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Abate, A.F., Barra, S., D’Aniello, F., Narducci, F. (2017). Two-Tier Image Features Clustering for Iris Recognition on Mobile. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_23
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