Abstract
Cross-view or heterogeneous face matching involves comparing two different views of the face modality such as two different spectrums or resolutions. In this research, we present two heterogeneity-aware subspace techniques, heterogeneous discriminant analysis (HDA) and its kernel version (KHDA) that encode heterogeneity in the objective function and yield a suitable projection space for improved performance. They can be applied on any feature to make it heterogeneity invariant. We next propose a face recognition framework that uses existing facial features along with HDA/KHDA for matching. The effectiveness of HDA and KHDA is demonstrated using both handcrafted and learned representations on three challenging heterogeneous cross-view face recognition scenarios: (i) visible to near-infrared matching, (ii) cross-resolution matching, and (iii) digital photo to composite sketch matching. It is observed that, consistently in all the case studies, HDA and KHDA help to reduce the heterogeneity variance, clearly evidenced in the improved results. Comparison with recent heterogeneous matching algorithms shows that HDA- and KHDA-based matching yields state-of-the-art or comparable results on all three case studies. The proposed algorithms yield the best rank-1 accuracy of 99.4% on the CASIA NIR-VIS 2.0 database, up to 100% on the CMU Multi-PIE for different resolutions, and 95.2% rank-10 accuracies on the e-PRIP database for digital to composite sketch matching.
Highlights
With increasing focus on security and surveillance, face biometrics has found several new applications and challenges in real-world scenarios
We present two subspace-based classifiers aiming at reducing the inter-view intra-class variability and increasing the inter-view inter-class variability for heterogeneous face recognition
We have proposed a discriminant analysis approach for heterogeneous face recognition
Summary
With increasing focus on security and surveillance, face biometrics has found several new applications and challenges in real-world scenarios. An image captured at a distance may have only 16 × 16 facial region for processing For these applications, the corresponding gallery or database image is generally a good quality mugshot image captured in controlled environments. The corresponding gallery or database image is generally a good quality mugshot image captured in controlled environments This leads to the challenge of heterogeneity in gallery and probe images. This figure showcases another interesting application of matching composite sketch images with digital face images. Compared to homogeneous face recognition, matching face images with different views is a challenging problem as heterogeneity leads to increase in the intra-class variability
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