Konferenzbeitrag
Deep Sparse Feature Selection and Fusion for Textured Contact Lens Detection
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Datum
2018
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Köllen Druck+Verlag GmbH
Zusammenfassung
Distinguishing between images of irises wearing textured lenses versus those wearing
transparent lenses or no lenses is a challenging problem due to the subtle and fine-grained visual
differences. Our approach builds upon existing hand-crafted image features and neural network architectures
by optimally selecting and combining the most useful set of features into a single model.
We build multiple, parallel sub-networks corresponding to the various feature descriptors and learn
the best subset of features through group sparsity. We avoid overfitting such a wide and deep model
through a selective transfer learning technique and a novel group Dropout regularization strategy.
This model achieves roughly a four times increase in performance over the state-of-the-art on three
benchmark textured lens datasets and equals the near-perfect state-of-the-art accuracy on two others.
Furthermore, the generic nature of the architecture allows it to be extended to other image features,
forms of spoofing attacks, or problem domains.