The Experts below are selected from a list of 759 Experts worldwide ranked by ideXlab platform
Anil K Jain - One of the best experts on this subject based on the ideXlab platform.
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IJCB - Fingerprint Spoof Detection: Temporal Analysis of Image Sequence
2020 IEEE International Joint Conference on Biometrics (IJCB), 2020Co-Authors: Tarang Chugh, Anil K JainAbstract:We utilize the dynamics involved in the imaging of a Fingerprint on a touch-based Fingerprint Reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers. Specifically, we utilize a deep learning-based architecture (CNN-LSTM) trained end-to-end using sequences of minutiae-centered local patches extracted from ten color frames captured on a COTS Fingerprint Reader. A time-distributed CNN (MobileNet-v1) extracts spatial features from each local patch, while a bi-directional LSTM layer learns the temporal relationship between the patches in the sequence. Experimental results on a database of 26, 650 live frames from 685 subjects (1,333 unique fingers), and 32,910 spoof frames of 7 spoof materials (with a total of 14 material variants), show that the proposed approach exceeds the state-of-the-art performance in both known-material and cross-material (generalization) scenarios. For instance, the proposed approach improves the state-of-the-art cross-material performance from TDR of 81.65% to 86.20% @ FDR = 0.2%.
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Fingerprint spoof detection temporal analysis of image sequence
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Tarang Chugh, Anil K JainAbstract:We utilize the dynamics involved in the imaging of a Fingerprint on a touch-based Fingerprint Reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers. Specifically, we utilize a deep learning-based architecture (CNN-LSTM) trained end-to-end using sequences of minutiae-centered local patches extracted from ten color frames captured on a COTS Fingerprint Reader. A time-distributed CNN (MobileNet-v1) extracts spatial features from each local patch, while a bi-directional LSTM layer learns the temporal relationship between the patches in the sequence. Experimental results on a database of 26,650 live frames from 685 subjects (1,333 unique fingers), and 32,910 spoof frames of 7 spoof materials (with 14 variants) shows the superiority of the proposed approach in both known-material and cross-material (generalization) scenarios. For instance, the proposed approach improves the state-of-the-art cross-material performance from TDR of 81.65% to 86.20% @ FDR = 0.2%.
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White-Box Evaluation of Fingerprint Matchers.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Steven A. Grosz, Nicholas G. Paulter, Joshua J. Engelsma, Anil K JainAbstract:Prevailing evaluations of Fingerprint recognition systems have been performed as end-to-end black-box tests of Fingerprint identification or verification accuracy. However, performance of the end-to-end system is subject to errors arising in any of the constituent modules, including: Fingerprint Reader, preprocessing, feature extraction, and matching. While a few studies have conducted white-box testing of the Fingerprint Reader and feature extraction modules of Fingerprint recognition systems, little work has been devoted towards white-box evaluations of the Fingerprint matching sub-module. We report results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf (COTS) state-of-the-art minutiae-based matchers in terms of their robustness against controlled perturbations (random noise, and non-linear distortions) introduced into the input minutiae feature sets. Experiments were conducted on 10,000 synthetically generated Fingerprints. Our white-box evaluations show performance comparisons between different minutiae-based matchers in the presence of various perturbations and non-linear distortion, which were not previously shown with black-box tests. Furthermore, our white-box evaluations reveal that the performance of Fingerprint minutiae matchers are more susceptible to non-linear distortion and missing minutiae than spurious minutiae and small positional displacements of the minutiae locations. The measurement uncertainty in Fingerprint matching is also developed.
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Infant-Prints: Fingerprints for Reducing Infant Mortality
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Joshua J. Engelsma, Anil K Jain, Prem Sewak Sudhish, Debayan Deb, Anjoo BhatnagerAbstract:In developing countries around the world, a multitude of infants continue to suffer and die from vaccine-preventable diseases, and malnutrition. Lamentably, the lack of any official identification documentation makes it exceedingly difficult to prevent these infant deaths. To solve this global crisis, we propose Infant-Prints which is comprised of (i) a custom, compact, low-cost (85 USD), high-resolution (1,900 ppi) Fingerprint Reader, (ii) a high-resolution Fingerprint matcher, and (iii) a mobile application for search and verification for the infant Fingerprint. Using Infant-Prints, we have collected a longitudinal database of infant Fingerprints and demonstrate its ability to perform accurate and reliable recognition of infants enrolled at the ages 0-3 months, in time for effective delivery of critical vaccinations and nutritional supplements (TAR=90% @ FAR = 0.1% for infants older than 8 weeks).
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RaspiReader: Open Source Fingerprint Reader
IEEE transactions on pattern analysis and machine intelligence, 2018Co-Authors: Joshua J. Engelsma, Kai Cao, Anil K JainAbstract:We open source an easy to assemble, spoof resistant, high resolution, optical Fingerprint Reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the Fingerprint research community a seamless and simple method for quickly prototyping new ideas involving Fingerprint Reader hardware. In particular, we posit that this open source Fingerprint Reader will facilitate the exploration of novel Fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for Fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) Fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical Readers. Finally, Fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS Reader verify the interoperability of the RaspiReader with existing COTS optical Readers.
Joshua J. Engelsma - One of the best experts on this subject based on the ideXlab platform.
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White-Box Evaluation of Fingerprint Matchers.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Steven A. Grosz, Nicholas G. Paulter, Joshua J. Engelsma, Anil K JainAbstract:Prevailing evaluations of Fingerprint recognition systems have been performed as end-to-end black-box tests of Fingerprint identification or verification accuracy. However, performance of the end-to-end system is subject to errors arising in any of the constituent modules, including: Fingerprint Reader, preprocessing, feature extraction, and matching. While a few studies have conducted white-box testing of the Fingerprint Reader and feature extraction modules of Fingerprint recognition systems, little work has been devoted towards white-box evaluations of the Fingerprint matching sub-module. We report results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf (COTS) state-of-the-art minutiae-based matchers in terms of their robustness against controlled perturbations (random noise, and non-linear distortions) introduced into the input minutiae feature sets. Experiments were conducted on 10,000 synthetically generated Fingerprints. Our white-box evaluations show performance comparisons between different minutiae-based matchers in the presence of various perturbations and non-linear distortion, which were not previously shown with black-box tests. Furthermore, our white-box evaluations reveal that the performance of Fingerprint minutiae matchers are more susceptible to non-linear distortion and missing minutiae than spurious minutiae and small positional displacements of the minutiae locations. The measurement uncertainty in Fingerprint matching is also developed.
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Infant-Prints: Fingerprints for Reducing Infant Mortality
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Joshua J. Engelsma, Anil K Jain, Prem Sewak Sudhish, Debayan Deb, Anjoo BhatnagerAbstract:In developing countries around the world, a multitude of infants continue to suffer and die from vaccine-preventable diseases, and malnutrition. Lamentably, the lack of any official identification documentation makes it exceedingly difficult to prevent these infant deaths. To solve this global crisis, we propose Infant-Prints which is comprised of (i) a custom, compact, low-cost (85 USD), high-resolution (1,900 ppi) Fingerprint Reader, (ii) a high-resolution Fingerprint matcher, and (iii) a mobile application for search and verification for the infant Fingerprint. Using Infant-Prints, we have collected a longitudinal database of infant Fingerprints and demonstrate its ability to perform accurate and reliable recognition of infants enrolled at the ages 0-3 months, in time for effective delivery of critical vaccinations and nutritional supplements (TAR=90% @ FAR = 0.1% for infants older than 8 weeks).
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RaspiReader: Open Source Fingerprint Reader
IEEE transactions on pattern analysis and machine intelligence, 2018Co-Authors: Joshua J. Engelsma, Kai Cao, Anil K JainAbstract:We open source an easy to assemble, spoof resistant, high resolution, optical Fingerprint Reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the Fingerprint research community a seamless and simple method for quickly prototyping new ideas involving Fingerprint Reader hardware. In particular, we posit that this open source Fingerprint Reader will facilitate the exploration of novel Fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for Fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) Fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical Readers. Finally, Fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS Reader verify the interoperability of the RaspiReader with existing COTS optical Readers.
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Fingerprint Match in Box
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Joshua J. Engelsma, Kai Cao, Anil K JainAbstract:We open source Fingerprint Match in Box, a complete end-to-end Fingerprint recognition system embedded within a 4 inch cube. Match in Box stands in contrast to a typical bulky and expensive proprietary Fingerprint recognition system which requires sending a Fingerprint image to an external host for processing and subsequent spoof detection and matching. In particular, Match in Box is a first of a kind, portable, low-cost, and easy-to-assemble Fingerprint Reader with an enrollment database embedded within the Reader's memory and open source Fingerprint spoof detector, feature extractor, and matcher all running on the Reader's internal vision processing unit (VPU). An onboard touch screen and rechargeable battery pack make this device extremely portable and ideal for applying both Fingerprint authentication (1:1 comparison) and Fingerprint identification (1:N search) to applications (vaccination tracking, food and benefit distribution programs, human trafficking prevention) in rural communities, especially in developing countries. We also show that Match in Box is suited for capturing neonate Fingerprints due to its high resolution (1900 ppi) cameras.
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Matching Fingerphotos to Slap Fingerprint Images.
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Tarang Chugh, Joshua J. Engelsma, Neeta Nain, Jake Kendall, Anil K JainAbstract:We address the problem of comparing fingerphotos, Fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based Fingerprint images. Development of robust versions of these technologies would enable the use of the billions of standard Android phones as biometric Readers through a simple software download, dramatically lowering the cost and complexity of deployment relative to using a separate Fingerprint Reader. Two fingerphoto apps running on Android phones and an optical slap Reader were utilized for Fingerprint collection of 309 subjects who primarily work as construction workers, farmers, and domestic helpers. Experimental results show that a True Accept Rate (TAR) of 95.79 at a False Accept Rate (FAR) of 0.1% can be achieved in matching fingerphotos to slaps (two thumbs and two index fingers) using a COTS Fingerprint matcher. By comparison, a baseline TAR of 98.55% at 0.1% FAR is achieved when matching Fingerprint images from two different contact-based optical Readers. We also report the usability of the two smartphone apps, in terms of failure to acquire rate and Fingerprint acquisition time. Our results show that fingerphotos are promising to authenticate individuals (against a national ID database) for banking, welfare distribution, and healthcare applications in developing countries.
Nicholas G. Paulter - One of the best experts on this subject based on the ideXlab platform.
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White-Box Evaluation of Fingerprint Matchers.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Steven A. Grosz, Nicholas G. Paulter, Joshua J. Engelsma, Anil K JainAbstract:Prevailing evaluations of Fingerprint recognition systems have been performed as end-to-end black-box tests of Fingerprint identification or verification accuracy. However, performance of the end-to-end system is subject to errors arising in any of the constituent modules, including: Fingerprint Reader, preprocessing, feature extraction, and matching. While a few studies have conducted white-box testing of the Fingerprint Reader and feature extraction modules of Fingerprint recognition systems, little work has been devoted towards white-box evaluations of the Fingerprint matching sub-module. We report results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf (COTS) state-of-the-art minutiae-based matchers in terms of their robustness against controlled perturbations (random noise, and non-linear distortions) introduced into the input minutiae feature sets. Experiments were conducted on 10,000 synthetically generated Fingerprints. Our white-box evaluations show performance comparisons between different minutiae-based matchers in the presence of various perturbations and non-linear distortion, which were not previously shown with black-box tests. Furthermore, our white-box evaluations reveal that the performance of Fingerprint minutiae matchers are more susceptible to non-linear distortion and missing minutiae than spurious minutiae and small positional displacements of the minutiae locations. The measurement uncertainty in Fingerprint matching is also developed.
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Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations
IEEE Transactions on Information Forensics and Security, 2018Co-Authors: Joshua J. Engelsma, Anil K Jain, Sunpreet S. Arora, Nicholas G. PaulterAbstract:We present the design and manufacturing of high-fidelity universal 3D Fingerprint targets, which can be imaged on a variety of Fingerprint sensing technologies, namely, capacitive, contact optical, and contactless optical. Universal 3D Fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of Fingerprint Readers but also the ability to conduct Fingerprint Reader interoperability studies. Fingerprint Reader interoperability refers to how robust Fingerprint recognition systems are to variations in the images acquired by different types of Fingerprint Readers. To build universal 3D Fingerprint targets, we adopt a molding and casting framework consisting of: 1) digital mapping of Fingerprint images to a negative mold; 2) CAD modeling a scaffolding system to hold the negative mold; 3) fabricating the mold and scaffolding system with a high resolution 3D printer; 4) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger; and 5) fabricating a 3D Fingerprint target using controlled casting. Our experiments conducted with personal identity verification and Appendix F certified optical (contact and contactless) and capacitive Fingerprint Readers demonstrate the usefulness of universal 3D Fingerprint targets for controlled and repeatable Fingerprint Reader evaluations and also Fingerprint Reader interoperability studies.
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Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Joshua J. Engelsma, Anil K Jain, Sunpreet S. Arora, Nicholas G. PaulterAbstract:We present the design and manufacturing of high fidelity universal 3D Fingerprint targets, which can be imaged on a variety of Fingerprint sensing technologies, namely capacitive, contact-optical, and contactless-optical. Universal 3D Fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of Fingerprint Readers, but also the ability to conduct Fingerprint Reader interoperability studies. Fingerprint Reader interoperability refers to how robust Fingerprint recognition systems are to variations in the images acquired by different types of Fingerprint Readers. To build universal 3D Fingerprint targets, we adopt a molding and casting framework consisting of (i) digital mapping of Fingerprint images to a negative mold, (ii) CAD modeling a scaffolding system to hold the negative mold, (iii) fabricating the mold and scaffolding system with a high resolution 3D printer, (iv) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger, and (v) fabricating a 3D Fingerprint target using controlled casting. Our experiments conducted with PIV and Appendix F certified optical (contact and contactless) and capacitive Fingerprint Readers demonstrate the usefulness of universal 3D Fingerprint targets for controlled and repeatable Fingerprint Reader evaluations and also Fingerprint Reader interoperability studies.
Jain, Anil K. - One of the best experts on this subject based on the ideXlab platform.
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White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbations
2020Co-Authors: Grosz, Steven A., Engelsma, Joshua J., Paulter Jr., Nicholas G., Jain, Anil K.Abstract:Prevailing evaluations of Fingerprint recognition systems have been performed as end-to-end black-box tests of Fingerprint identification or authentication accuracy. However, performance of the end-to-end system is subject to errors arising in any of its constituent modules, including: Fingerprint scanning, preprocessing, feature extraction, and matching. Conversely, white-box evaluations provide a more granular evaluation by studying the individual sub-components of a system. While a few studies have conducted stand-alone evaluations of the Fingerprint Reader and feature extraction modules of Fingerprint recognition systems, little work has been devoted towards white-box evaluations of the Fingerprint matching module. We report results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf (COTS) minutiae-based matchers in terms of their robustness against controlled perturbations (random noise and non-linear distortions) introduced into the input minutiae feature sets. Our white-box evaluations reveal that the performance of Fingerprint minutiae matchers are more susceptible to non-linear distortion and missing minutiae than spurious minutiae and small positional displacements of the minutiae locations
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Infant-ID: Fingerprints for Global Good
2020Co-Authors: Engelsma, Joshua J., Cao Kai, Deb Debayan, Bhatnagar Anjoo, Sudhish, Prem S., Jain, Anil K.Abstract:In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition. Lamentably, the lack of official identification documentation makes it exceedingly difficult to track which infants have been vaccinated and which infants have received nutritional supplements. Answering these questions could prevent this infant suffering and premature death around the world. To that end, we propose Infant-Prints, an end-to-end, low-cost, infant Fingerprint recognition system. Infant-Prints is comprised of our (i) custom built, compact, low-cost (85 USD), high-resolution (1,900 ppi), ergonomic Fingerprint Reader, and (ii) high-resolution infant Fingerprint matcher. To evaluate the efficacy of Infant-Prints, we collected a longitudinal infant Fingerprint database captured in 4 different sessions over a 12-month time span (December 2018 to January 2020), from 315 infants at the Saran Ashram Hospital, a charitable hospital in Dayalbagh, Agra, India. Our experimental results demonstrate, for the first time, that Infant-Prints can deliver accurate and reliable recognition (over time) of infants enrolled between the ages of 2-3 months, in time for effective delivery of vaccinations, healthcare, and nutritional supplements (TAR=95.2% @ FAR = 1.0% for infants aged 8-16 weeks at enrollment and authenticated 3 months later).Comment: 16 pages, 16 figure
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Fingerprint Spoof Detection: Temporal Analysis of Image Sequence
2019Co-Authors: Chugh Tarang, Jain, Anil K.Abstract:We utilize the dynamics involved in the imaging of a Fingerprint on a touch-based Fingerprint Reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers. Specifically, we utilize a deep learning-based architecture (CNN-LSTM) trained end-to-end using sequences of minutiae-centered local patches extracted from ten color frames captured on a COTS Fingerprint Reader. A time-distributed CNN (MobileNet-v1) extracts spatial features from each local patch, while a bi-directional LSTM layer learns the temporal relationship between the patches in the sequence. Experimental results on a database of 26,650 live frames from 685 subjects (1,333 unique fingers), and 32,910 spoof frames of 7 spoof materials (with 14 variants) shows the superiority of the proposed approach in both known-material and cross-material (generalization) scenarios. For instance, the proposed approach improves the state-of-the-art cross-material performance from TDR of 81.65% to 86.20% @ FDR = 0.2%.Comment: 8 page
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OCT Fingerprints: Resilience to Presentation Attacks
2019Co-Authors: Chugh Tarang, Jain, Anil K.Abstract:Optical coherent tomography (OCT) Fingerprint technology provides rich depth information, including internal Fingerprint (papillary junction) and sweat (eccrine) glands, in addition to imaging any fake layers (presentation attacks) placed over finger skin. Unlike 2D surface Fingerprint scans, additional depth information provided by the cross-sectional OCT depth profile scans are purported to thwart Fingerprint presentation attacks. We develop and evaluate a presentation attack detector (PAD) based on deep convolutional neural network (CNN). Input data to CNN are local patches extracted from the cross-sectional OCT depth profile scans captured using THORLabs Telesto series spectral-domain Fingerprint Reader. The proposed approach achieves a TDR of 99.73% @ FDR of 0.2% on a database of 3,413 bonafide and 357 PA OCT scans, fabricated using 8 different PA materials. By employing a visualization technique, known as CNN-Fixations, we are able to identify the regions in the OCT scan patches that are crucial for Fingerprint PAD detection.Comment: Fingerprint presentation attack detection, OCT scanner; 9 pages, 8 figure
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Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
2019Co-Authors: Engelsma, Joshua J., Jain, Anil K.Abstract:Prevailing Fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide Fingerprints from a set of known spoof Fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live Fingerprints such that once the concept of "live" has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live Fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader Fingerprint Reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively
Sunpreet S. Arora - One of the best experts on this subject based on the ideXlab platform.
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Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations
IEEE Transactions on Information Forensics and Security, 2018Co-Authors: Joshua J. Engelsma, Anil K Jain, Sunpreet S. Arora, Nicholas G. PaulterAbstract:We present the design and manufacturing of high-fidelity universal 3D Fingerprint targets, which can be imaged on a variety of Fingerprint sensing technologies, namely, capacitive, contact optical, and contactless optical. Universal 3D Fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of Fingerprint Readers but also the ability to conduct Fingerprint Reader interoperability studies. Fingerprint Reader interoperability refers to how robust Fingerprint recognition systems are to variations in the images acquired by different types of Fingerprint Readers. To build universal 3D Fingerprint targets, we adopt a molding and casting framework consisting of: 1) digital mapping of Fingerprint images to a negative mold; 2) CAD modeling a scaffolding system to hold the negative mold; 3) fabricating the mold and scaffolding system with a high resolution 3D printer; 4) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger; and 5) fabricating a 3D Fingerprint target using controlled casting. Our experiments conducted with personal identity verification and Appendix F certified optical (contact and contactless) and capacitive Fingerprint Readers demonstrate the usefulness of universal 3D Fingerprint targets for controlled and repeatable Fingerprint Reader evaluations and also Fingerprint Reader interoperability studies.
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Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Joshua J. Engelsma, Anil K Jain, Sunpreet S. Arora, Nicholas G. PaulterAbstract:We present the design and manufacturing of high fidelity universal 3D Fingerprint targets, which can be imaged on a variety of Fingerprint sensing technologies, namely capacitive, contact-optical, and contactless-optical. Universal 3D Fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of Fingerprint Readers, but also the ability to conduct Fingerprint Reader interoperability studies. Fingerprint Reader interoperability refers to how robust Fingerprint recognition systems are to variations in the images acquired by different types of Fingerprint Readers. To build universal 3D Fingerprint targets, we adopt a molding and casting framework consisting of (i) digital mapping of Fingerprint images to a negative mold, (ii) CAD modeling a scaffolding system to hold the negative mold, (iii) fabricating the mold and scaffolding system with a high resolution 3D printer, (iv) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger, and (v) fabricating a 3D Fingerprint target using controlled casting. Our experiments conducted with PIV and Appendix F certified optical (contact and contactless) and capacitive Fingerprint Readers demonstrate the usefulness of universal 3D Fingerprint targets for controlled and repeatable Fingerprint Reader evaluations and also Fingerprint Reader interoperability studies.
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giving infants an identity Fingerprint sensing and recognition
Information and Communication Technologies and Development, 2016Co-Authors: Anil K Jain, Sunpreet S. Arora, Prem Sewak Sudhish, Lacey Bestrowden, Anjoo Bhatnagar, Yoshinori KodaAbstract:There is a growing demand for biometrics-based recognition of children for a number of applications, particularly in developing countries where children do not have any form of identification. These applications include tracking child vaccination schedules, identifying missing children, preventing fraud in food subsidies, and preventing newborn baby swaps in hospitals. Our objective is to develop a Fingerprint-based identification system for infants (age range: 0-12 months)1. Our ongoing research has addressed the following issues: (i) design of a compact, comfortable, high-resolution (>1,000 ppi) Fingerprint Reader; (ii) image enhancement algorithms to improve quality of infant Fingerprint images; and (iii) collection of longitudinal infant Fingerprint data to evaluate identification accuracy over time. This collaboration between Michigan State University, Dayalbagh Educational Institute, Saran Ashram Hospital, Agra, India and NEC Corporation, has demonstrated the feasibility of recognizing infants older than 4 weeks using Fingerprints.
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ICTD - Giving Infants an Identity: Fingerprint Sensing and Recognition
Proceedings of the Eighth International Conference on Information and Communication Technologies and Development, 2016Co-Authors: Anil K Jain, Kai Cao, Sunpreet S. Arora, Prem Sewak Sudhish, Anjoo Bhatnagar, Lacey Best-rowden, Yoshinori KodaAbstract:There is a growing demand for biometrics-based recognition of children for a number of applications, particularly in developing countries where children do not have any form of identification. These applications include tracking child vaccination schedules, identifying missing children, preventing fraud in food subsidies, and preventing newborn baby swaps in hospitals. Our objective is to develop a Fingerprint-based identification system for infants (age range: 0-12 months)1. Our ongoing research has addressed the following issues: (i) design of a compact, comfortable, high-resolution (>1,000 ppi) Fingerprint Reader; (ii) image enhancement algorithms to improve quality of infant Fingerprint images; and (iii) collection of longitudinal infant Fingerprint data to evaluate identification accuracy over time. This collaboration between Michigan State University, Dayalbagh Educational Institute, Saran Ashram Hospital, Agra, India and NEC Corporation, has demonstrated the feasibility of recognizing infants older than 4 weeks using Fingerprints.