Gender classification from multispectral periocular images

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