Sex-Prediction from Periocular Images across Multiple Sensors and Spectra
This work addresses the problem of realistic sex-prediction in biometrics for security or identification applications, but it is incremental as it builds on existing methods with new data and scenarios.
The paper tackled sex-prediction from periocular images using convolutional neural networks trained on fused datasets from multiple sensors and spectra, achieving average accuracies of 85% within spectra, 82% across spectra, and 83% for multi-spectral prediction.
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 cross-sensor 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.