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Periocular recognition: how much facial expressions affect performance?

, , , , and . Pattern Anal. Appl., 19 (2): 517-530 (2016)

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Fusing color and shape descriptors in the recognition of degraded iris images acquired at visible wavelengths., and . Comput. Vis. Image Underst., 116 (2): 167-178 (2012)Robust Periocular Recognition by Fusing Sparse Representations of Color and Geometry Information., , , and . J. Signal Process. Syst., 82 (3): 403-417 (2016)A robust eye-corner detection method for real-world data., and . IJCB, page 1-7. IEEE Computer Society, (2011)BioHDD: a dataset for studying biometric identification on heavily degraded data., , and . IET Biom., 4 (1): 1-9 (2015)Quis-Campi: Extending in the Wild Biometric Recognition to Surveillance Environments., , , , , , and . ICIAP Workshops, volume 9281 of Lecture Notes in Computer Science, page 59-68. Springer, (2015)Segmenting the periocular region using a hierarchical graphical model fed by texture / shape information and geometrical constraints., , and . IJCB, page 1-7. IEEE, (2014)On the Role of Interpolation in the Normalization of Non-ideal Visible Wavelength Iris Images., and . CIS (1), page 315-319. IEEE Computer Society, (2009)Iris Recognition: Preliminary Assessment about the Discriminating Capacity of Visible Wavelength Data., , , and . ISM, page 324-329. IEEE Computer Society, (2010)A Comparative Analysis of Two Approaches to Periocular Recognition in Mobile Scenarios., , , , , and . ISVC (2), volume 9475 of Lecture Notes in Computer Science, page 268-280. Springer, (2015)Fusing iris and periocular information for cross-sensor recognition., , , and . Pattern Recognit. Lett., (2015)