Sex-Prediction from Periocular Images Across Multiple Sensors and Spectra

Abstract

In this paper, we provide a comprehensive analysis of periocular-based sex-prediction (commonly referred to as gender classification) using state-of-the-art machine learning techniques. In order to reflect a more challenging scenario where periocular images are likely to be obtained from an unknown source, i.e. sensor, convolutional neural networks are trained on fused sets composed of several near-infrared (NIR) and visible wavelength (VW) image databases. In a crosssensor scenario within each spectrum an average classification accuracy of approximately 85% is achieved. When sex-prediction is performed across spectra an average classification accuracy of about 82% is obtained. Finally, a multi-spectral sex-prediction yields a classification accuracy of 83% on average. Compared to proposed works, obtained results provide a more realistic estimation of the feasibility to predict a subject's sex from the periocular region.

Impact of Occlusion Masks on Gender Classification from Iris Texture

“…Gender classification using iris images has been addressed in several studies following two main approaches [4]: classification using periocular iris images, and classification using normalized iris images. Classification using periocular iris images usually yields results close to and above 80% accuracy and has been repeated successfully over time [4,[9][10][11][12][15][16][17][18][19][20][21][22][23][24][25][26][27]. Using periocular images for gender classification benefits from additional gender cues that are not present in the iris.…”