Automatic Latent Fingerprint Segmentation

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

doi:10.1109/btas.2018.8698544

Cited by 23 publications

(7 citation statements)

References 15 publications

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“…As an addendum, deep networks have also been used to improve specific sub-modules of fingerprint recognition systems such as segmentation [30], [31], [32], [33], orientation field estimation [34], [35], [36], minutiae extraction [37], [38], [39], and minutiae descriptor extraction [40]. However, these works all still operate within the conventional paradigm of extracting an unordered, variable length set of minutiae for fingerprint matching.…”

Section: Prior Workmentioning

confidence: 99%

Learning a Fixed-Length Fingerprint Representation

Engelsma

Cao²,

Jain

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation. The compact, DeepPrint representation has several advantages over the prevailing variable length minutiae representation which (i) requires computationally expensive graph matching techniques, (ii) is difficult to secure using strong encryption schemes (e.g. homomorphic encryption), and (iii) has low discriminative power in poor quality fingerprints where minutiae extraction is unreliable. We benchmark DeepPrint against two top performing COTS SDKs (Verifinger and Innovatrics) from the NIST and FVC evaluations. Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to the top COTS matcher, but it is significantly faster (DeepPrint: 98.80% in 0.3 seconds vs. COTS A: 98.85% in 27 seconds). To the best of our knowledge, the DeepPrint representation is the most compact and discriminative fixed-length fingerprint representation reported in the academic literature.

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Section: Prior Workmentioning

confidence: 99%

Learning a Fixed-Length Fingerprint Representation

Engelsma

Cao²,

Jain

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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“…To reduce human involvement, some level of automation was introduced in the fingerprint identification process. Automatic ROI cropping (Choi et al, 2012 ; Zhang et al, 2013 ; Cao et al, 2014 ; Nguyen et al, 2018a ), ridge-flow estimation (Feng et al, 2013 ; Cao et al, 2014 , 2015 ; Yang et al, 2014 ), and ridge-enhancement (Feng et al, 2013 ; Li et al, 2018 ; Prabhu et al, 2018 ) methods were proposed by various researchers. The Descriptor-Based Hough Transform (DBHT) (Paulino et al, 2013 ) was proposed to align and match the fingerprints.…”

Section: Literature Surveymentioning

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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“…Latent segmentation is a method to partition data based on statistical information Cohen and Ramaswamy (1998), and is primarily seen in marketing approaches based on consumer segments Bhatnagar and Ghose (2004); Swait (1994). In recent years, latent segmentation using a deep neural network has been proposed and applied to various tasks Angueira et al (2019); Ezeobiejesi and Bhanu (2017); Nguyen et al (2018); Villarejo Ramos et al (2019).…”

Section: Latent Segmentationmentioning

confidence: 99%

Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning

Maeda

deGraw²,

Kitano

et al. 2020

JRFM

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While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through observation of publicly available unlabeled market data alone. Several methods have been suggested in the literature using classification methods based on summary trading statistics, as well as using inverse reinforcement learning methods to infer the reward function underlying trader behavior. Our primary contribution is to propose an alternative neural network based multi-modal imitation learning model which performs latent segmentation of stock trading strategies. As a result that the segmentation in the latent space is optimized according to individual reward functions underlying the order submission behaviors across each segment, our results provide interpretable classifications and accurate predictions that outperform other methods in major classification indicators as verified on historical orderbook data from January 2018 to August 2019 obtained from the Tokyo Stock Exchange. By further analyzing the behavior of various trader segments, we confirmed that our proposed segments behaves in line with real-market investor sentiments.

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Automatic Latent Fingerprint Segmentation

Cited by 23 publications

References 15 publications

Learning a Fixed-Length Fingerprint Representation

Learning a Fixed-Length Fingerprint Representation

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

Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning

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