Latent fingerprint from multiple surfaces: Database and quality analysis
17 Dec 2015-pp 1-6
TL;DR: This research creates a novel multi-surface latent fingerprint database and makes it publicly available for the research community to characterize the quality of latent fingerprints and compute the matching performance to analyze the effect of different surfaces.
Abstract: Latent fingerprints are lifted from multiple types of surfaces, which vary in material type, texture, color, and shape. These differences in the surfaces introduce significant intra-class variations in the lifted prints such as availability of partial print, background noise, and poor ridge structure quality. Due to these observed variations, the overall quality and the matching performance of latent fingerprints vary with respect to surface properties. Thus, characterizing the performance of latent fingerprints according to the surfaces they are lifted from is an important research problem that needs attention. In this research, we create a novel multi-surface latent fingerprint database and make it publicly available for the research community. The database consists of 551 latent fingerprints from 51 subjects lifted from eight different surfaces. Using existing algorithms, we characterize the quality of latent fingerprints and compute the matching performance to analyze the effect of different surfaces.
Citations
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TL;DR: An extensive review of the work done by eminent researchers in the development of an automated latent fingerprint identification system is provided.
Abstract: Latent fingerprints are considered as one of the important evidences obtained from the site of crime. The process of developing, acquiring, processing and matching of latent fingerprints is different from the inked or live-scan fingerprints. Automated identification of latent fingerprints is still in its nascent phase when compared with the Automatic Fingerprint Identification System (AFIS) used by the police department. This paper provides an extensive review of the work done by eminent researchers in the development of an automated latent fingerprint identification system.
38 citations
01 Jan 2019
TL;DR: A Generative Adversarial Network based latent fingerprint enhancement algorithm is proposed to enhance the poor quality ridges and predict the ridge information and helps the standard feature extraction and matching algorithms to boost latent fingerprints matching performance.
Abstract: Latent fingerprints recognition is very useful in law enforcement and forensics applications. However, automated matching of latent fingerprints with a gallery of live scan images is very challenging due to several compounding factors such as noisy background, poor ridge structure, and overlapping unstructured noise. In order to efficiently match latent fingerprints, an effective enhancement module is a necessity so that it can facilitate correct minutiae extraction. In this research, we propose a Generative Adversarial Network based latent fingerprint enhancement algorithm to enhance the poor quality ridges and predict the ridge information. Experiments on two publicly available datasets, IIITD-MOLF and IIITD-MSLFD show that the proposed enhancement algorithm improves the fingerprints quality while preserving the ridge structure. It helps the standard feature extraction and matching algorithms to boost latent fingerprints matching performance.
37 citations
Cites methods from "Latent fingerprint from multiple su..."
...In this experiment, the latent fingerprints of IIITD-MSLFD have been matched with the fingerprints acquired through a sensor....
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...The accuracy on IIITD-MSLF database is lower than the IIITDMOLF database, this can be attributed to varying and complex backgrounds in IIITD-MSLFD compared to the IIITDMOLF....
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...IIITD-MSLFD has latent fingerprint impressions of 51 subjects acquired from 8 different surfaces such as ceramic mug, compact disc, hardbound book cover, and transparent glass....
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...The performance of the proposed algorithm has been evaluated on two publicly available datasets: IIITDMulti-Optical Latent Fingerprint (MOLF) database [21] and IIITD-Multi-Surface Latent Fingerprint Database (MSLFD) [17]....
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...We have evaluated the proposed algorithm on IIITD Multisurface Dataset [21] which has latent fingerprints extracted from eight different surfaces as well as the IIITD MOLF Datatset [17] which has over 4000 latent impressions....
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TL;DR: Level 3 features, particularly pores are extracted using Fully Convolution Neural Network (FCN) from the collected latent fingerprints using the RUVIS, which plays a significant role in matching these latent impressions with plain impressions.
Abstract: Latent fingerprint is considered as the key evidence during crime scene investigations. Various powder and chemical methods are available for visualizing the latent fingerprints as these finger impressions are not directly visible through the naked eye. However, these methods may damage the finger impressions in case if they are not properly lifted and handled carefully. Preserving the evidential value of the located latent fingerprints, hence becomes pivotal for analyzing and identifying the suspected individual. Nowadays, optical touchless technology is being prevalent for developing and visualizing the latent finger impressions. Reflected Ultra Violet Imaging System (RUVIS) is one such optical touchless device. There are number of powder based latent fingerprint databases available. However, database of latent fingerprints using optical touchless technology is not available in the literature. The paper presents the latent fingerprint database developed and captured using the touch-less acquisition device (RUVIS). Extraction of level 3 features from the latent fingerprints plays a significant role in matching these latent impressions with plain impressions. Further in this paper, level 3 features, particularly pores are extracted using Fully Convolution Neural Network (FCN) from the collected latent fingerprints using the RUVIS.
8 citations
21 Mar 2021
TL;DR: The proposed latent fingerprint enhancement model preserves ridge structure including minutiae, and discusses the role of training data i.e. various noise models which should be considered for modeling a latent fingerprint, during training a GAN.
Abstract: Latent fingerprints are the fingerprints which are left unintentionally on a surface while touching it These are of great interest for the forensics experts for criminal identification Latent fingerprints usually possess high nonlinear distortion Furthermore, these may be overlapping with background text or other fingerprints These fingerprints can be extracted from different surfaces leading to the varying background The presence of structured and unstructured background noise adversely affects minutiae (ridge bifurcation/ridge ending) extraction in latent fingerprints which in turn leads to poor matching performance A latent fingerprint enhancement algorithm removes the background noise and predicts the missing ridge information It also improves the ridge clarity which helps to improve minutiae extraction and thereby improving matching performance Traditionally, latent fingerprints are enhanced by approximating the orientation field and then applying contextual filtering using the approximated orientations However, recently the attention has been shifted towards developing models which can directly denoise the fingerprints and reconstruct the missing ridge structure without explicitly estimating the orientation field
Inspired by the success of Generative Adversarial Network (GAN) in image processing applications, we propose a GAN-based latent fingerprint enhancement model However, one of the key issues with GANs is that they are difficult to train Through this work, we contribute our efforts towards sharing details on successfully training a GAN The proposed latent fingerprint enhancement model preserves the ridge structure including minutiae We discuss the role of training data, ie, various noise models which should be considered for modelling a latent fingerprint, during training a GAN In addition to this, we discuss the significance of choice of loss function and the role of hyper-parameters such as batch size, weight of each loss term, and number of epochs for training the GAN We evaluate the proposed enhancement model on publicly available latent databases: Indraprastha Institute of Information Technology Delhi Multi-sensor Optical and Latent Fingerprint (IIITD-MOLF) and Indraprastha Institute of Information Technology Delhi Multi-surface Latent Fingerprint (IIITD-MSLF)
8 citations
TL;DR: In this article , a predictive framework for automated fingermark quality assessment (AFQA) is proposed, which bridges the gap between the classic machine learning approach with handcrafted features and the modern deep learning paradigm, evaluate the advantages and disadvantages of these methodologies, and provide the rationale and direction for future development of AFQA methods.
Abstract: The quality assessment of fingermarks (latent fingerprints) is an essential part of a forensic investigation. It indicates how valuable the fingermarks are as forensic evidence, it determines how they should be further processed, and it correlates with the likelihood of successful identification, i.e., finding a matching fingerprint in a reference database. Since the environments in which fingermarks are found are not controlled, this task proves challenging even with modern machine learning solutions. In this work, we propose a predictive framework for automated fingermark quality assessment (AFQA). With this iteration of AFQA, we bridge the gap between the classic machine learning approach with handcrafted features and the modern deep learning paradigm, evaluate the advantages and disadvantages of these methodologies, and provide the rationale and direction for future development of AFQA methods. We present a significantly improved AFQA toolbox and provide a quality aggregation method capable of fusing together multiple predicted quality values from an ensemble of quality assessment models. The proposed ensemble approach provides improved prediction performance while reducing processing time compared to existing state-of-the-art solutions.
5 citations
References
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25 Oct 2010
TL;DR: VLFeat is an open and portable library of computer vision algorithms that includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization.
Abstract: VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.
3,417 citations
"Latent fingerprint from multiple su..." refers methods in this paper
...[13] Gabor Response (Gstd) 614400 Orientation θ = [0, 45, 90, 135] VLFEAT [23] Keypoint (DSIFT) 115072 step = 24, size = 24, binSize = 8, magnif = 3...
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...Five sets of fingerprint features are as follows: (i) ridge quality [24] is based on the connectivity of the ridge flow with respect to its neighbourhood, (ii) spatial domain quality [7] computes the ridge clarity as a function of the principal Eigen value of a 2D tensor, (iii) power spectrum [10] computes the log power spectral density of the Fourier response of the fingerprint image, (iv) Gabor response approach [13] calculates the standard deviation of the responses of a filter of Gabor bank, and (v) DSIFT approach [23] computes the Dense SIFT features from predefined keypoints on the fingerprint image....
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Book•
01 Jan 2009
TL;DR: Strengthening Forensic Science in the United States: A Path Forward provides a detailed plan for addressing these needs and suggests the creation of a new government entity, the National Institute of Forensic Science, to establish and enforce standards within the forensic science community as discussed by the authors.
Abstract: Scores of talented and dedicated people serve the forensic science community, performing vitally important work. However, they are often constrained by lack of adequate resources, sound policies, and national support. It is clear that change and advancements, both systematic and scientific, are needed in a number of forensic science disciplines to ensure the reliability of work, establish enforceable standards, and promote best practices with consistent application. Strengthening Forensic Science in the United States: A Path Forward provides a detailed plan for addressing these needs and suggests the creation of a new government entity, the National Institute of Forensic Science, to establish and enforce standards within the forensic science community. The benefits of improving and regulating the forensic science disciplines are clear: assisting law enforcement officials, enhancing homeland security, and reducing the risk of wrongful conviction and exoneration. Strengthening Forensic Science in the United States gives a full account of what is needed to advance the forensic science disciplines, including upgrading of systems and organizational structures, better training, widespread adoption of uniform and enforceable best practices, and mandatory certification and accreditation programs. While this book provides an essential call-to-action for congress and policy makers, it also serves as a vital tool for law enforcement agencies, criminal prosecutors and attorneys, and forensic science educators.
900 citations
TL;DR: The Minutia Cylinder-Code is introduced, a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles and the feasibility of obtaining a very effective fingerprint recognition implementation for light architectures is demonstrated.
Abstract: In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures.
565 citations
TL;DR: Four different fingerprint matching algorithms are combined using the proposed scheme to improve the accuracy of a fingerprint verification system and it is shown that a combination of multiple impressions or multiple fingers improves the verification performance by more than 4% and 5%, respectively.
Abstract: A scheme is proposed for classifier combination at decision level which stresses the importance of classifier selection during combination. The proposed scheme is optimal (in the Neyman–Pearson sense) when sufficient data are available to obtain reasonable estimates of the join densities of classifier outputs. Four different fingerprint matching algorithms are combined using the proposed scheme to improve the accuracy of a fingerprint verification system. Experiments conducted on a large fingerprint database (∼2700 fingerprints) confirm the effectiveness of the proposed integration scheme. An overall matching performance increase of ∼3% is achieved. We further show that a combination of multiple impressions or multiple fingers improves the verification performance by more than 4% and 5%, respectively. Analysis of the results provide some insight into the various decision-level classifier combination strategies.
371 citations
TL;DR: Both quality indices for fingerprint images are developed and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.
Abstract: The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.
292 citations
"Latent fingerprint from multiple su..." refers background or methods in this paper
...[7] Spatial Domain Quality (Qs, ki) 2304 block = (32× 32), q = 1 Guan et al....
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...Five sets of fingerprint features are as follows: (i) ridge quality [24] is based on the connectivity of the ridge flow with respect to its neighbourhood, (ii) spatial domain quality [7] computes the ridge clarity as a function of the principal Eigen value of a 2D tensor, (iii) power spectrum [10] computes the log power spectral density of the Fourier response of the fingerprint image, (iv) Gabor response approach [13] calculates the standard deviation of the responses of a filter of Gabor bank, and (v) DSIFT approach [23] computes the Dense SIFT features from predefined keypoints on the fingerprint image....
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