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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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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DOI: https://doi.org/10.1007/978-981-15-4015-8_1
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