2019/05/01 by Juan Tapia, Christian Rathgeb, Tapia, Juan +3
Computer Science · Medicine · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Nasal Surgery and Airway Studies #cs.CV
paper · pdf · doi:10.48550/arxiv.1905.00396
Pre-print version of Paper presented at Proc. International Workshop on Ubiquitous implicit Biometrics and health signals monitoring for person-centric applications (UBIO 18), 2018
arxiv created 2019/05/01 · openalex publication_date 2019/05/01 · arxiv updated 2019/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.