  Mobile biometric approaches provide the convenience of secure authentication
with an omnipresent technology. However, this brings an additional challenge of
recognizing biometric patterns in unconstrained environment including
variations in mobile camera sensors, illumination conditions, and capture
distance. To address the heterogeneous challenge, this research presents a
novel heterogeneity aware loss function within a deep learning framework. The
effectiveness of the proposed loss function is evaluated for periocular
biometrics using the CSIP, IMP and VISOB mobile periocular databases. The
results show that the proposed algorithm yields state-of-the-art results in a
heterogeneous environment and improves generalizability for cross-database
experiments.
