Gender classification from multispectral periocular images
作者： Juan Tapia , Ignacio Viedma
DOI: 10.1109/BTAS.2017.8272774
关键词:
摘要: Gender classification from multispectral periocular and iris images is a new topic on soft-biometric research. The feature extracted RGB Near Infrared Images shows complementary information independent of the spectrum images. This paper that we canfusion these improving accuracy gender classification. Most methods reported in literature has used face databases all features for purposes. Experimental results suggest: (a) Features different scales can perform better than using only one single scale; (b) performed VIS NIR; c) fusion spectral NIR allows improve accuracy; (c) selection applied to select relevant d) Our 90% competitive with state art.
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