Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
| Autor: | Anil K. Jain, Kai Cao, Dinh-Luan Nguyen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Minutiae 021110 strategic defence & security studies Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 0211 other engineering and technologies Pattern recognition 02 engineering and technology Image segmentation Convolutional neural network ComputingMethodologies_PATTERNRECOGNITION Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing Segmentation Artificial intelligence Precision and recall business |
| Zdroj: | ICB |
| DOI: | 10.1109/icb2018.2018.00013 |
| Popis: | We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNet Accepted to International Conference on Biometrics (ICB 2018) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|