Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272737
Xiaochen Zhu, Larry L Tang, Elham Tabassi
Existing statistical methods for estimating the log-likelihood ratio from biometric scores include parametric estimation, kernel density estimation, and recently adopted logistic regression estimation. There has been a growing interest to study the repeatability and reproducibility of these methods on biometric datasets after the 2009 National Research Council report [15] and the 2016 President's Council of Advisors on Science and Technology report [1]. For a statistical forensic evaluation method to be repeatable, it needs to generate consistent log-likelihood ratios for various sample size ratios between the genuine (mated) and imposter (non-mated) scores from the same database. It is a well known fact, that for logistic regression methods, the estimated intercept value depends on the sample size ratio between the two groups. Therefore, when computing log-likelihood ratios using logistic regression estimation, different genuine and impostor sample size ratios could result in different log-likelihood ratio values. We performed extensive simulations and used face and fingerprint biometric datasets to investigate repeatability and reproducibility of existing log-likelihood ratio estimation methods.
{"title":"Repeatability and reproducibility of forensic likelihood ratio methods when sample size ratio varies","authors":"Xiaochen Zhu, Larry L Tang, Elham Tabassi","doi":"10.1109/BTAS.2017.8272737","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272737","url":null,"abstract":"Existing statistical methods for estimating the log-likelihood ratio from biometric scores include parametric estimation, kernel density estimation, and recently adopted logistic regression estimation. There has been a growing interest to study the repeatability and reproducibility of these methods on biometric datasets after the 2009 National Research Council report [15] and the 2016 President's Council of Advisors on Science and Technology report [1]. For a statistical forensic evaluation method to be repeatable, it needs to generate consistent log-likelihood ratios for various sample size ratios between the genuine (mated) and imposter (non-mated) scores from the same database. It is a well known fact, that for logistic regression methods, the estimated intercept value depends on the sample size ratio between the two groups. Therefore, when computing log-likelihood ratios using logistic regression estimation, different genuine and impostor sample size ratios could result in different log-likelihood ratio values. We performed extensive simulations and used face and fingerprint biometric datasets to investigate repeatability and reproducibility of existing log-likelihood ratio estimation methods.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122265099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272754
Akshay Agarwal, Richa Singh, Mayank Vatsa, A. Noore
Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations. While such enablements have positive effects, as a negative side, now anyone can digitally attack face (biometric) recognition systems. For example, face swapping application of Snapchat can easily create “swapped” identities and circumvent face recognition system. This research presents a novel database, termed as SWAPPED — Digital Attack Video Face Database, prepared using Snap chat's application which swaps/stitches two faces and creates videos. The database contains bonafide face videos and face swapped videos of multiple subjects. Baseline face recognition experiments using commercial system shows over 90% rank-1 accuracy when attack videos are used as probe. As a second contribution, this research also presents a novel Weighted Local Magnitude Pattern feature descriptor based presentation attack detection algorithm which outperforms several existing approaches.
{"title":"SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern","authors":"Akshay Agarwal, Richa Singh, Mayank Vatsa, A. Noore","doi":"10.1109/BTAS.2017.8272754","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272754","url":null,"abstract":"Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations. While such enablements have positive effects, as a negative side, now anyone can digitally attack face (biometric) recognition systems. For example, face swapping application of Snapchat can easily create “swapped” identities and circumvent face recognition system. This research presents a novel database, termed as SWAPPED — Digital Attack Video Face Database, prepared using Snap chat's application which swaps/stitches two faces and creates videos. The database contains bonafide face videos and face swapped videos of multiple subjects. Baseline face recognition experiments using commercial system shows over 90% rank-1 accuracy when attack videos are used as probe. As a second contribution, this research also presents a novel Weighted Local Magnitude Pattern feature descriptor based presentation attack detection algorithm which outperforms several existing approaches.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134306561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272743
Vahid Mirjalili, A. Ross
While the primary purpose for collecting biometric data (such as face images, iris, fingerprints, etc.) is for person recognition, yet recent advances in machine learning has shown the possibility of extracting auxiliary information from biometric data such as age, gender, health attributes, etc. These auxiliary attributes are sometimes referred to as soft biometrics. This automatic extraction of soft biometric attributes can happen without the user's agreement, thereby raising several privacy concerns. In this work, we design a technique that modifies a face image such that its gender as assessed by a gender classifier is perturbed, while its biometric utility as assessed by a face matcher is retained. Given an arbitrary biometric matcher and an attribute classifier, the proposed method systematically perturbs the input image such that the output of the attribute classifier is confounded, while the output of the biometric matcher is not significantly impacted. Experimental analysis convey the efficacy of the scheme in imparting gender privacy to face images.
{"title":"Soft biometric privacy: Retaining biometric utility of face images while perturbing gender","authors":"Vahid Mirjalili, A. Ross","doi":"10.1109/BTAS.2017.8272743","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272743","url":null,"abstract":"While the primary purpose for collecting biometric data (such as face images, iris, fingerprints, etc.) is for person recognition, yet recent advances in machine learning has shown the possibility of extracting auxiliary information from biometric data such as age, gender, health attributes, etc. These auxiliary attributes are sometimes referred to as soft biometrics. This automatic extraction of soft biometric attributes can happen without the user's agreement, thereby raising several privacy concerns. In this work, we design a technique that modifies a face image such that its gender as assessed by a gender classifier is perturbed, while its biometric utility as assessed by a face matcher is retained. Given an arbitrary biometric matcher and an attribute classifier, the proposed method systematically perturbs the input image such that the output of the attribute classifier is confounded, while the output of the biometric matcher is not significantly impacted. Experimental analysis convey the efficacy of the scheme in imparting gender privacy to face images.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272745
T. Chugh, Kai Cao, Anil K. Jain
The individuality of fingerprints is being leveraged for a plethora of day-to-day applications, ranging from unlocking a smartphone to international border security. While the primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate and generalizable algorithms for detecting fingerprint spoof attacks. We propose a deep convolutional neural network based approach utilizing local patches extracted around fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state of the art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, the proposed approach achieves a 69% reduction in average classification error for spoof detection under both known material and cross-material scenarios on LivDet 2015 datasets.
{"title":"Fingerprint spoof detection using minutiae-based local patches","authors":"T. Chugh, Kai Cao, Anil K. Jain","doi":"10.1109/BTAS.2017.8272745","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272745","url":null,"abstract":"The individuality of fingerprints is being leveraged for a plethora of day-to-day applications, ranging from unlocking a smartphone to international border security. While the primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate and generalizable algorithms for detecting fingerprint spoof attacks. We propose a deep convolutional neural network based approach utilizing local patches extracted around fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state of the art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, the proposed approach achieves a 69% reduction in average classification error for spoof detection under both known material and cross-material scenarios on LivDet 2015 datasets.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"25 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133572664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272683
T. Neal, D. Woodard
While mobile devices are no longer a new technology, using the data generated from the use of these devices for security purposes has just recently been explored. Current methods, such as passwords, are quickly becoming antiquated, lacking the robustness, accuracy, and convenience desired to serve as reliable security measures. Since, researchers have resorted to alternative techniques, such as measurements obtained from keyboard interactions and movement, and behavioral interactions, such as application usage. However, practical implementations require further evaluation of circumvention. Thus, this work thoroughly analyzes various threats against mobile devices which use mobile device usage data as behavioral biometrics for authentication. Experimental results indicate that an outsider with a certain level of knowledge regarding the behavior of the device's owner poses a great security threat. Possible countermeasures to prevent such attacks are also provided.
{"title":"Spoofing analysis of mobile device data as behavioral biometric modalities","authors":"T. Neal, D. Woodard","doi":"10.1109/BTAS.2017.8272683","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272683","url":null,"abstract":"While mobile devices are no longer a new technology, using the data generated from the use of these devices for security purposes has just recently been explored. Current methods, such as passwords, are quickly becoming antiquated, lacking the robustness, accuracy, and convenience desired to serve as reliable security measures. Since, researchers have resorted to alternative techniques, such as measurements obtained from keyboard interactions and movement, and behavioral interactions, such as application usage. However, practical implementations require further evaluation of circumvention. Thus, this work thoroughly analyzes various threats against mobile devices which use mobile device usage data as behavioral biometrics for authentication. Experimental results indicate that an outsider with a certain level of knowledge regarding the behavior of the device's owner poses a great security threat. Possible countermeasures to prevent such attacks are also provided.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133567749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272705
Tateo Ogane, I. Echizen
As fingerprint authentication technology becomes more advanced, it is being used in a growing range of personal devices such as PCs and smartphones. On the other hand, it has been pointed out that digital cameras can be used to capture people's fingerprints remotely, leaving them at risk of illegal log-ins or identity theft. This article shows how photographs can be processed to facilitate illegal fingerprint authentication. To prevent this from happening, we also propose a method that defeats the use of surreptitious photography to replicate fingerprints from photographs while still allowing contact-based fingerprint sensors to respond normally. We have verified that an implementation of the proposed method called Biometric Jammer can be worn to effectively prevent the illegal acquisition of fingerprints by surreptitious photography without inconveniencing the user or preventing the use of legitimate fingerprint authentication devices.
{"title":"Biometric Jammer: Preventing surreptitious fingerprint photography without inconveniencing users","authors":"Tateo Ogane, I. Echizen","doi":"10.1109/BTAS.2017.8272705","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272705","url":null,"abstract":"As fingerprint authentication technology becomes more advanced, it is being used in a growing range of personal devices such as PCs and smartphones. On the other hand, it has been pointed out that digital cameras can be used to capture people's fingerprints remotely, leaving them at risk of illegal log-ins or identity theft. This article shows how photographs can be processed to facilitate illegal fingerprint authentication. To prevent this from happening, we also propose a method that defeats the use of surreptitious photography to replicate fingerprints from photographs while still allowing contact-based fingerprint sensors to respond normally. We have verified that an implementation of the proposed method called Biometric Jammer can be worn to effectively prevent the illegal acquisition of fingerprints by surreptitious photography without inconveniencing the user or preventing the use of legitimate fingerprint authentication devices.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124943618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272677
K. Lai, S. Yanushkevich, V. Shmerko
This paper concerns with facial-based watch list technology as a component of automated border control machines deployed in e-borders. The key task of the watch list technology is to mitigate effects of mis-identification and impersonation. To address this problem, we developed a novel cost-based model of traveler risk assessment and proved its efficiency via intensive experiments using large-scale facial databases. The results of this study are applicable to any biometric modality to be used in watch list technology.
{"title":"Risk assessment in the face-based watchlist screening in e-borders","authors":"K. Lai, S. Yanushkevich, V. Shmerko","doi":"10.1109/BTAS.2017.8272677","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272677","url":null,"abstract":"This paper concerns with facial-based watch list technology as a component of automated border control machines deployed in e-borders. The key task of the watch list technology is to mitigate effects of mis-identification and impersonation. To address this problem, we developed a novel cost-based model of traveler risk assessment and proved its efficiency via intensive experiments using large-scale facial databases. The results of this study are applicable to any biometric modality to be used in watch list technology.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131678710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272694
Lingfeng Zhang, I. Kakadiaris
This paper focuses on improving the performance of current convolutional neural networks in face recognition without changing the network architecture. We propose a hierarchical framework that builds chains of local binary neural networks after one global neural network over all the class labels, Local Classifier Chains based Convolutional Neural Networks (LCC-CNN). Two different criteria based on a similarity matrix and confusion matrix are introduced to select binary label pairs to create local deep networks. To avoid error propagation, each testing sample travels through one global model and a local classifier chain to obtain its final prediction. The proposed framework has been evaluated with UHDB31 and CASIA-WebFace datasets. The experimental results indicate that our framework achieves better performance when compared with using only baseline methods as the global deep network. The accuracy is improved by 2.7% and 0.7% on the two datasets, respectively.
{"title":"Local classifier chains for deep face recognition","authors":"Lingfeng Zhang, I. Kakadiaris","doi":"10.1109/BTAS.2017.8272694","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272694","url":null,"abstract":"This paper focuses on improving the performance of current convolutional neural networks in face recognition without changing the network architecture. We propose a hierarchical framework that builds chains of local binary neural networks after one global neural network over all the class labels, Local Classifier Chains based Convolutional Neural Networks (LCC-CNN). Two different criteria based on a similarity matrix and confusion matrix are introduced to select binary label pairs to create local deep networks. To avoid error propagation, each testing sample travels through one global model and a local classifier chain to obtain its final prediction. The proposed framework has been evaluated with UHDB31 and CASIA-WebFace datasets. The experimental results indicate that our framework achieves better performance when compared with using only baseline methods as the global deep network. The accuracy is improved by 2.7% and 0.7% on the two datasets, respectively.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121021468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272682
Qiang Chen, Yunhong Wang, Zheng Liu, Qingjie Liu, Di Huang
In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.
{"title":"Feature map pooling for cross-view gait recognition based on silhouette sequence images","authors":"Qiang Chen, Yunhong Wang, Zheng Liu, Qingjie Liu, Di Huang","doi":"10.1109/BTAS.2017.8272682","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272682","url":null,"abstract":"In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123338638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272728
Kai Cao, Anil K. Jain
Large scale fingerprint recognition systems have been deployed worldwide not only in law enforcement but also in many civilian applications. Thus, it is of great value o identify a query fingerprint in a large background finger-print database both effectively and efficiently based on indexing strategies. The published indexing algorithms do not meet the requirements, especially at low penetrate rates, because of the difficulty in extracting reliable minutiae and other features in low quality fingerprint images. We propose a Convolutional Neural Network (ConvNet) based fingerprint indexing algorithm. An orientation field dictionary is learned to align fingerprints in a unified coordinate system and a large longitudinal fingerprint database, where each finger has multiple impressions over time, is used to train the ConvNet. Experimental results on NIST SD4 and NIST SD14 show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. Further indexing results on an augmented gallery set of 250K rolled prints demonstrate the scalability of the proposed algorithm. At a penetrate rate of 1%, a score-level fusion of the proposed indexing and a state-of-the-art COTS SDK provides 97.8% rank-1 identification accuracy with a 100-fold reduction in the search space.
{"title":"Fingerprint indexing and matching: An integrated approach","authors":"Kai Cao, Anil K. Jain","doi":"10.1109/BTAS.2017.8272728","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272728","url":null,"abstract":"Large scale fingerprint recognition systems have been deployed worldwide not only in law enforcement but also in many civilian applications. Thus, it is of great value o identify a query fingerprint in a large background finger-print database both effectively and efficiently based on indexing strategies. The published indexing algorithms do not meet the requirements, especially at low penetrate rates, because of the difficulty in extracting reliable minutiae and other features in low quality fingerprint images. We propose a Convolutional Neural Network (ConvNet) based fingerprint indexing algorithm. An orientation field dictionary is learned to align fingerprints in a unified coordinate system and a large longitudinal fingerprint database, where each finger has multiple impressions over time, is used to train the ConvNet. Experimental results on NIST SD4 and NIST SD14 show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. Further indexing results on an augmented gallery set of 250K rolled prints demonstrate the scalability of the proposed algorithm. At a penetrate rate of 1%, a score-level fusion of the proposed indexing and a state-of-the-art COTS SDK provides 97.8% rank-1 identification accuracy with a 100-fold reduction in the search space.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124275001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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