Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge (1712.09401v1)

Abstract: 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

• The paper introduces MinutiaeNet, a dual-stage CNN framework that integrates fingerprint domain techniques to enhance minutiae detection.
• CoarseNet employs residual learning with enhanced image features for initial candidate localization, while FineNet refines minutiae using an Inception-ResNet approach.
• Empirical results on NIST SD27 and FVC2004 datasets show improved precision, recall, and F1 scores, validating its impact on biometric recognition.

Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

The paper "Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge" by Dinh-Luan Nguyen, Kai Cao, and Anil K. Jain presents a novel approach to fingerprint minutiae extraction utilizing deep learning techniques. This paper addresses a long-standing challenge in the biometrics domain: the accurate detection of minutiae from latent and plain fingerprint images, which is crucial for improving fingerprint recognition systems' reliability and accuracy.

Overview and Methodology

The authors propose MinutiaeNet, a fully automated minutiae extraction framework that integrates Convolutional Neural Networks (CNNs) with domain-specific knowledge from the fingerprint domain. The framework consists of two primary components: CoarseNet and FineNet.

• CoarseNet: This component integrates residual learning and traditional fingerprint domain methods such as enhanced images, segmentation maps, and orientation fields. It generates an initial minutiae score map that helps locate candidate minutiae points within the fingerprint image. The CoarseNet uses a deep residual learning architecture to process the fingerprint images and employs a multi-scale approach to enhance segmentation and orientation estimations, crucial for improved minutiae detection.
• FineNet: Built upon an Inception-ResNet v1 architecture, FineNet further refines the candidate minutiae locations determined by CoarseNet. It performs robust classification of minutiae based on patch regions centered around initial candidate points, thus improving the overall precision and recall of the minutiae detection process. FineNet also deals with issues related to intra-class variability and distortion present in the fingerprint images.

The framework strategically employs fingerprint domain knowledge, enhancing traditional feature extraction with robust deep learning capabilities. Importantly, the authors address the challenge of false positive reduction using a novel non-maximum suppression technique, improving precision and recall without relying on hard thresholds.

Empirical Results

The empirical evaluation conducted on publicly available datasets, namely NIST SD27 and FVC 2004, demonstrates the framework's efficacy over existing benchmark models. The experimental results indicate that MinutiaeNet achieves superior performance in terms of precision, recall, and F1 score. For example, under specific settings, the F1 score on the FVC 2004 dataset reaches 0.837, and on the NIST SD27 dataset, it reaches 0.734. These metrics represent significant improvements over alternative minutiae extraction methods, highlighting the framework's robust handling of latent fingerprint variability.

Implications and Future Directions

The implications of this work are noteworthy from both practical and theoretical perspectives. Practically, the development of an automated and accurate minutiae extractor presents significant enhancements to fingerprint recognition systems, which can be vital in security, forensic, and identification applications. Theoretically, the integration of fingerprint domain knowledge within a deep learning framework showcases an effective method for utilizing domain-specific features in neural network architectures, potentially informing advancements in other biometric and image recognition domains.

Looking forward, there are several avenues for future developments and refinements in the context of this research:

1. Incorporating Larger Datasets: Enhancing the dataset, particularly with more latent fingerprints, would likely improve the model's generalization capabilities further.
2. Enhancement of Contextual Features: Developing robust context descriptors around minutiae could further boost accuracy, especially in heavily corrupted latent prints.
3. End-to-End System Integration: The integration of the presented minutiae extraction framework into a comprehensive end-to-end fingerprint recognition system could significantly streamline the entire matching process.
4. Time Efficiency Optimization: The processing time per image remains an area for further optimization, which could enhance real-time applicability.

In summary, MinutiaeNet represents a pivotal step forward in the domain of fingerprint recognition by leveraging deep networks supplemented with domain-specific knowledge. The approach not only advances the state of the art in minutiae detection but also sets a precedent for future research to build upon.