Synthesis-Guided Feature Learning for Cross-Spectral Periocular
Recognition
- URL: http://arxiv.org/abs/2111.08738v1
- Date: Tue, 16 Nov 2021 19:22:20 GMT
- Title: Synthesis-Guided Feature Learning for Cross-Spectral Periocular
Recognition
- Authors: Domenick Poster and Nasser Nasrabadi
- Abstract summary: We propose a novel approach to cross-spectral periocular verification.
It primarily focuses on learning a mapping from visible and NIR periocular images to a shared latent representational subspace.
We show the auxiliary image reconstruction task results in learning a more discriminative, domain-invariant subspace.
- Score: 1.52292571922932
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A common yet challenging scenario in periocular biometrics is cross-spectral
matching - in particular, the matching of visible wavelength against
near-infrared (NIR) periocular images. We propose a novel approach to
cross-spectral periocular verification that primarily focuses on learning a
mapping from visible and NIR periocular images to a shared latent
representational subspace, and supports this effort by simultaneously learning
intra-spectral image reconstruction. We show the auxiliary image reconstruction
task (and in particular the reconstruction of high-level, semantic features)
results in learning a more discriminative, domain-invariant subspace compared
to the baseline while incurring no additional computational or memory costs at
test-time. The proposed Coupled Conditional Generative Adversarial Network
(CoGAN) architecture uses paired generator networks (one operating on visible
images and the other on NIR) composed of U-Nets with ResNet-18 encoders trained
for feature learning via contrastive loss and for intra-spectral image
reconstruction with adversarial, pixel-based, and perceptual reconstruction
losses. Moreover, the proposed CoGAN model beats the current state-of-art
(SotA) in cross-spectral periocular recognition. On the Hong Kong PolyU
benchmark dataset, we achieve 98.65% AUC and 5.14% EER compared to the SotA EER
of 8.02%. On the Cross-Eyed dataset, we achieve 99.31% AUC and 3.99% EER versus
SotA EER of 4.39%.
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