Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

Nguyen, Duc Dung; Cao, Kai; Jain, Anil K.

doi:10.1109/icb2018.2018.00013

Cited by 78 publications

(65 citation statements)

References 28 publications

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“…MinutiaeNet (Nguyen et al, 2018b) NIST SD27 is a criminal fingerprint database and contains 258 fingerprint images obtained from 258 persons. It forms a total class of 258 fingerprint images with 88 Good, 85 Bad, and 85 Ugly images.…”

Section: Resultsmentioning

confidence: 99%

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Deshpande¹,

Malemath

Patil

et al. 2020

Front. Robot. AI

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Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

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Section: Resultsmentioning

confidence: 99%

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Deshpande¹,

Malemath

Patil

et al. 2020

Front. Robot. AI

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show abstract

“…In latent fingerprint recognition literature, it is a common practice to use a patch based approach in various modules, (i.e. minutiae extraction [16] and enhancement [19,3]). In such cases, the input latent is divided into multiple overlapping patches at different locations.…”

Section: Related Workmentioning

confidence: 99%

“…We also adopt the strategy of combining high-level layers with low-level layers [9,4,16] to get finer detail prediction while maintaining high-level semantic interpretation as multi-scale prediction.…”

Section: Nonwarp-roialignmentioning

confidence: 99%

Automatic Latent Fingerprint Segmentation

Nguyen

Cao

Jain

2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

Self Cite

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We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet. SegFinNet takes a latent image as an input and outputs a binary mask highlighting the friction ridge pattern. Our algorithm combines fully convolutional neural network and detection-based approaches to process the entire input latent image in one shot instead of using latent patches. Experimental results on three different latent databases (i.e. NIST SD27, WVU, and an operational forensic database) show that SegFinNet outperforms both human markup for latents and the state-of-the-art latent segmentation algorithms. We further show that this improved cropping boosts the hit rate of a latent fingerprint matcher.

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“…Thirdly, they compared systematically factored directional filtering with other similar fingerprint segmentation approaches and obtained better performance. In 2018, Dinh-Luan Nguyen, Kai Cao, and Anil K. Jain proposed a fingerprint image segmentation method and a deep network model SegFinNet [8] in the field of latent fingerprints. Serafim, P. B. S., Medeiros, A. G., Rego, P. A., et al, 2019 [9] proposed an ROI segmentation method based on CNN to classify image patches and apply filtering techniques for improving the quality of the ROI.…”

Section: Introductionmentioning

confidence: 99%

XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net

Wan

et al. 2020

Sensors

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Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.

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Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

Cited by 78 publications

References 28 publications

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Automatic Latent Fingerprint Segmentation

XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net

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