Meta Learning for Few-Shot One-class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting. Our code is available at https://github.com/gdahia/meta_occ

Improving Fingerprint Pore Detection with a Small FCN

In this work, we investigate if previously proposed CNNs for fingerprint pore detection overestimate the number of required model parameters for this task. We show that this is indeed the case by proposing a fully convolutional neural network that has significantly fewer parameters. We evaluate this model using a rigorous and reproducible protocol, which was, prior to our work, not available to the community. Using our protocol, we show that the proposed model, when combined with post-processing, performs better than previous methods, albeit being much more efficient. All our code is available at https://github.com/gdahia/fingerprint-pore-detection

CNN-based Pore Detection and Description for High-Resolution Fingerprint Recognition

High-resolution fingerprint recognition usually relies on sophisticated matching algorithms to match hand-crafted keypoint, usually pores, descriptors. In this work, we improve the state-of-the-art results in a public benchmark by using instead a CNN pore descriptor with a simpler matching algorithm. We describe how aligning images allows learning keypoint descriptors when the dataset does not provide keypoint identity annotations. We also propose a new pore detector, together with a detailed evaluation protocol for this task. All our code is available at https://github.com/gdahia/high-res-fingerprint-recognition