Abstract
Periocular characteristics is gaining prominence in biometric systems and surveillance systems that operate either in NIR spectrum or visible spectrum. While the ocular information can be well utilized, there exists a challenge to compare images from different spectra such as Near-Infra-Red (NIR) versus Visible spectrum (VIS). In addition, the ocular biometric templates from both NIR and VIS domain need to be protected after the extraction of features to avoid the leakage or linkability of biometric data. In this work, we explore a new approach based on anchored kernel hashing to obtain a cancelable biometric template that is both discriminative for recognition purposes while preserving privacy. The key benefit is that the proposed approach not only works for both NIR and the Visible spectrum, it can also be used with good accuracy for cross-spectral protected template comparison. Through the set of experiments using a cross-spectral periocular database, we demonstrate the performance with \(EER=1.39\%\) and \(EER=1.61\%\) for NIR and VIS protected templates respectively. We further present a set of cross-spectral template comparison by comparing the protected templates from one spectrum to another spectra to demonstrate the applicability of the proposed approach.
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Notes
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Available by request at www.crosseyed.eu.
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It has to be noted that the performance reported here cannot be directly compared with performance reported earlier due to changes in number of images in enrolment and probe set. A slight change in the performance can be observed as compared to earlier reported results.
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Acknowledgement
This work is partially carried out under the funding of the Research Council of Norway (Grant No. IKTPLUSS 248030/O70).
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Raja, K.B., Raghavendra, R., Busch, C. (2019). Anchored Kernel Hashing for Cancelable Template Protection for Cross-Spectral Periocular Data. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_10
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