Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs
This addresses a practical biometric verification problem for security and identification applications, though it appears incremental in scope.
The paper tackled cross-domain face verification between ID documents and self-portrait photos by using image photometric adjustment, data standardization, and deep learning to reduce domain shift effects. Results on a novel dataset of 50 individuals were promising, indicating the approach warrants further investigation.
Cross-domain biometrics has been emerging as a new necessity, which poses several additional challenges, including harsh illumination changes, noise, pose variation, among others. In this paper, we explore approaches to cross-domain face verification, comparing self-portrait photographs ("selfies") to ID documents. We approach the problem with proper image photometric adjustment and data standardization techniques, along with deep learning methods to extract the most prominent features from the data, reducing the effects of domain shift in this problem. We validate the methods using a novel dataset comprising 50 individuals. The obtained results are promising and indicate that the adopted path is worth further investigation.