Abstract
Iris and periocular (collectively termed as ocular) biometric modalities have been the most sought for modalities, due to their increased discrimination ability. Moreover, both these modalities can be captured through a single acquisition setup, leading to ease in user interaction. Owing to these advantages, this paper presents two local descriptors, namely statistical and transform-based descriptors, to investigate the worthiness of ocular recognition. The first descriptor uses mean and variance formulae after two levels of partitioning to extract the distinctive features. Whereas, second descriptor comprises of implementation of curvelet transform, followed by polynomial fitting, to extract the features. Individual iris and periocular features are combined through simple concatenation operation. The experiments performed on the challenging Cross-Eyed database, vindicate the efficacy of both the employed descriptors in same-spectral as well as cross-spectral matching scenarios.
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References
Alonso-Fernandez, F., Bigun, J.: A survey on periocular biometrics research. Pattern Recogn. Lett. 82, 92–105 (2016)
Alonso-Fernandez, F., Mikaelyan, A., Bigun, J.: Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images. In: 3rd International Workshop on Biometrics and Forensics (IWBF), pp. 1–6 (2015)
Arivazhagan, S., Ganesan, L., Priyal, S.P.: Texture classification using Gabor wavelets based rotation invariant features. Pattern Recogn. Lett. 27(16), 1976–1982 (2006)
Candès, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)
Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recogn. 36(2), 279–291 (2003)
Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Joshi, A., Gangwar, A.K., Saquib, Z.: Person recognition based on fusion of iris and periocular biometrics. In: Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 57–62 (2012)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Park, U., Jillela, R.R., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forensics Secur. 6(1), 96–106 (2011)
Raghavendra, R., Raja, K.B., Yang, B., Busch, C.: Combining iris and periocular recognition using light field camera. In: 2nd IAPR Asian Conference on Pattern Recognition, ACPR, pp. 155–159 (2013)
Raja, K.B., Raghavendra, R., Busch, C.: Binarized statistical features for improved iris and periocular recognition in visible spectrum. In: 2nd International Workshop on Biometrics and Forensics (IWBF), pp. 1–6 (2014)
Ross, A., et al.: Matching highly non-ideal ocular images: an information fusion approach. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 446–453 (2012)
Sequeira, A.F., et al.: Cross-eyed - cross-spectral iris/periocular recognition database and competition. In: 5th International Conference of the Biometrics Special Interest Group (BIOSIG 2016), Sequeira 2016, pp. 1–5 (2016). https://sites.google.com/site/crossspectrumcompetition/home
Sharma, A., Verma, S., Vatsa, M., Singh, R.: On cross spectral periocular recognition. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5007–5011 (2014)
Vyas, R., Kanumuri, T., Sheoran, G.: Cross spectral iris recognition for surveillance based applications. Multimedia Tools Appl. 78(5), 5681–5699 (2018). https://doi.org/10.1007/s11042-018-5689-y
Vyas, R., Kanumuri, T., Sheoran, G., Dubey, P.: Efficient features for smartphone-based iris recognition. Turk. J. Electr. Eng. Comput. Sci. 27(3), 1589–1602 (2019)
Vyas, R., Kanumuri, T., Sheoran, G., Dubey, P.: Efficient iris recognition through curvelet transform and polynomial fitting. Optik 185, 859–867 (2019)
Woodard, D.L., Pundlik, S., Miller, P., Jillela, R., Ross, A.: On the fusion of periocular and iris biometrics in non-ideal imagery. In: Proceedings - 20th International Conference on Pattern Recognition (ICPR), pp. 201–204 (2010)
Zand, M., Doraisamy, S., Halin, A.A., Mustaffa, M.R.: Texture classification and discrimination for region-based image retrieval. J. Vis. Commun. Image Represent. 26, 305–316 (2015)
Zhao, Z., Kumar, A.: An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: IEEE International Conference on Computer Vision, pp. 3828–3836 (2015)
Zhao, Z., Kumar, A.: Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network. IEEE Trans. Inf. Forensics Secur. 12(5), 1017–1030 (2017)
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Vyas, R., Kanumuri, T., Sheoran, G., Dubey, P. (2020). Towards Ocular Recognition Through Local Image Descriptors. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_1
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