Soft biometric classification using local appearance periocular region features

This paper investigates the effectiveness of local appearance features such as Local Binary Patterns, Histograms of Oriented Gradient, Discrete Cosine Transform, and Local Color Histograms extracted from periocular region images for soft classification on gender and ethnicity. These features are classified by Artificial Neural Network or Support Vector Machine. Experiments are performed on visible and near-IR spectrum images derived from FRGC and MBGC datasets. For 4232 FRGC images of 404 subjects, we obtain baseline gender and ethnicity classifications of 97.3% and 94%. For 350 MBGC images of 60 subjects, we obtain baseline gender and ethnicity results of 90% and 89%.

► We investigate the use of the periocular region as a soft biometric classifier. ► We perform experiments on gender and ethnicity over lighting and expression changes. ► Periocular region performs comparably with face on gender and ethnic classifications. ► Periocular performance is comparable to face under variable lighting and expression.