Pub Date : 2015-05-19DOI: 10.1109/ICB.2015.7139063
Phumpat Ruangsakul, V. Areekul, Krisada Phromsuthirak, Arucha Rungchokanun
In this work, we present a latent fingerprint segmentation algorithm based on spatial-frequency domain analysis. The algorithm arranges the overlapped block-based Fourier coefficients into groups of frequency and orientation subbands, called Rearranged Fourier Subband (RFS). The RFS reveals latent fingerprint spectra in only a limited number of subbands. The algorithm then boosts, sorts, and extracts, from complex background and noise, the latent fingerprint spectra in the RFS subbands. Several experiments are evaluated based on ground truth comparison, feature extraction, and latent matching on the NIST SD27 latent database. Our experimental results show that the proposed algorithm achieves better accuracy compared to those of the published automatic segmentation algorithms.
{"title":"Latent fingerprints segmentation based on Rearranged Fourier Subbands","authors":"Phumpat Ruangsakul, V. Areekul, Krisada Phromsuthirak, Arucha Rungchokanun","doi":"10.1109/ICB.2015.7139063","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139063","url":null,"abstract":"In this work, we present a latent fingerprint segmentation algorithm based on spatial-frequency domain analysis. The algorithm arranges the overlapped block-based Fourier coefficients into groups of frequency and orientation subbands, called Rearranged Fourier Subband (RFS). The RFS reveals latent fingerprint spectra in only a limited number of subbands. The algorithm then boosts, sorts, and extracts, from complex background and noise, the latent fingerprint spectra in the RFS subbands. Several experiments are evaluated based on ground truth comparison, feature extraction, and latent matching on the NIST SD27 latent database. Our experimental results show that the proposed algorithm achieves better accuracy compared to those of the published automatic segmentation algorithms.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116932740","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139101
Caue Zaghetto, A. Zaghetto, F. Vidal, Luiz H. M. Aguiar
This paper presents a method based on Artificial Neural Network that evaluates the rotational bad-positioning of fingers on touchless multiview fingerprinting devices. The objective is to determine whether the finger is rotated or not, since a proper positioning of the finger is mandatory for high fingerprint matching rates. A test set of 9000 acquired images has being used to train, validate and test the proposed multilayer Artificial Neural Network classifier. To our knowledge, there is no definitive method that addressed the problem of fingerprint quality on touchless multiview scanners. The proposed finger rotation detection here presented is one of the steps that must be taken into account if a future automatic image quality assessment method is to be considered. Average results show that: (a) our classifier correctly identifies bad-positioning in approximately 94% of cases; and (b) if bad-positioning is detected, the rotation angle is correctly estimated in 90% evaluations.
{"title":"Touchless multiview fingerprint quality assessment: rotational bad-positioning detection using Artificial Neural Networks","authors":"Caue Zaghetto, A. Zaghetto, F. Vidal, Luiz H. M. Aguiar","doi":"10.1109/ICB.2015.7139101","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139101","url":null,"abstract":"This paper presents a method based on Artificial Neural Network that evaluates the rotational bad-positioning of fingers on touchless multiview fingerprinting devices. The objective is to determine whether the finger is rotated or not, since a proper positioning of the finger is mandatory for high fingerprint matching rates. A test set of 9000 acquired images has being used to train, validate and test the proposed multilayer Artificial Neural Network classifier. To our knowledge, there is no definitive method that addressed the problem of fingerprint quality on touchless multiview scanners. The proposed finger rotation detection here presented is one of the steps that must be taken into account if a future automatic image quality assessment method is to be considered. Average results show that: (a) our classifier correctly identifies bad-positioning in approximately 94% of cases; and (b) if bad-positioning is detected, the rotation angle is correctly estimated in 90% evaluations.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133507166","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139094
Emma Taborsky, Kristen C. Allen, Austin Blanton, Anil K. Jain, Brendan Klare
As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.
{"title":"Annotating Unconstrained Face Imagery: A scalable approach","authors":"Emma Taborsky, Kristen C. Allen, Austin Blanton, Anil K. Jain, Brendan Klare","doi":"10.1109/ICB.2015.7139094","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139094","url":null,"abstract":"As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131483499","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139076
John V. Monaco, G. Perez, C. Tappert, Patrick A. H. Bours, Soumik Mondal, S. Rajkumar, A. Morales, Julian Fierrez, J. Ortega-Garcia
This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.
{"title":"One-handed Keystroke Biometric Identification Competition","authors":"John V. Monaco, G. Perez, C. Tappert, Patrick A. H. Bours, Soumik Mondal, S. Rajkumar, A. Morales, Julian Fierrez, J. Ortega-Garcia","doi":"10.1109/ICB.2015.7139076","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139076","url":null,"abstract":"This work presents the results of the One-handed Keystroke Biometric Identification Competition (OhKBIC), an official competition of the 8th IAPR International Conference on Biometrics (ICB). A unique keystroke biometric dataset was collected that includes freely-typed long-text samples from 64 subjects. Samples were collected to simulate normal typing behavior and the severe handicap of only being able to type with one hand. Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally-typed and one-handed samples. Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system similar to Kaggle. The classification results and top performing strategies are described.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408875","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139069
J. Bringer, Constance Morel, C. Rathgeb
This paper analyses the unlinkability and the irreversibility of the iris biometric template protection system based on Bloom filters introduced at ICB 2013. Hermans et al. presented at BIOSIG 2014 an attack on the unlinkability of these templates. In the worst case, their attack succeeds with probability at least 96%. But in their attack, they assume protected templates generated from the same iriscode. In this paper, we analyze unlinkability on protected templates generated with two different iriscodes coming from the same iris, and we moreover introduce irreversibility analysis that exploits non-uniformity of the data. Our experiments thus practically demonstrate new vulnerabilities of the scheme.
{"title":"Security analysis of Bloom filter-based iris biometric template protection","authors":"J. Bringer, Constance Morel, C. Rathgeb","doi":"10.1109/ICB.2015.7139069","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139069","url":null,"abstract":"This paper analyses the unlinkability and the irreversibility of the iris biometric template protection system based on Bloom filters introduced at ICB 2013. Hermans et al. presented at BIOSIG 2014 an attack on the unlinkability of these templates. In the worst case, their attack succeeds with probability at least 96%. But in their attack, they assume protected templates generated from the same iriscode. In this paper, we analyze unlinkability on protected templates generated with two different iriscodes coming from the same iris, and we moreover introduce irreversibility analysis that exploits non-uniformity of the data. Our experiments thus practically demonstrate new vulnerabilities of the scheme.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140022","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139115
Jing Li, Li Zhang, Dong Guo, Shaojie Zhuo, T. Sim
Identical twins pose an interesting challenge for recognition systems due to their similar appearance. Although various biometrics have been proposed for the problem, existing works are quite limited due to the difficulty of obtaining a twins database. To encourage the methods for twins recognition and make a fair comparison of them by using the same database, we collected an audio-visual twins database at the Sixth Mojiang International Twins Festival held on 1 May 2010, China. Our database contains 39 pairs of twins in total, including Chinese, American and Russian subjects. This database contains several face images, facial motion videos and audio records for each subject. In this paper, we describe the collection procedure, organization of the database, and usage method of the database. We also show our experiments on face verification, facial motion verification and speaker verification for twins to provide usage examples of the database.
{"title":"Audio-visual twins database","authors":"Jing Li, Li Zhang, Dong Guo, Shaojie Zhuo, T. Sim","doi":"10.1109/ICB.2015.7139115","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139115","url":null,"abstract":"Identical twins pose an interesting challenge for recognition systems due to their similar appearance. Although various biometrics have been proposed for the problem, existing works are quite limited due to the difficulty of obtaining a twins database. To encourage the methods for twins recognition and make a fair comparison of them by using the same database, we collected an audio-visual twins database at the Sixth Mojiang International Twins Festival held on 1 May 2010, China. Our database contains 39 pairs of twins in total, including Chinese, American and Russian subjects. This database contains several face images, facial motion videos and audio records for each subject. In this paper, we describe the collection procedure, organization of the database, and usage method of the database. We also show our experiments on face verification, facial motion verification and speaker verification for twins to provide usage examples of the database.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129999819","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139106
B H Shekar, R. Bharathi, J. Kittler, Y. Vizilter, Leonid Mestestskiy
In this paper, we present a grid structured morphological pattern spectrum based approach for off-line signature verification. The proposed approach has three major phases: preprocessing, feature extraction and verification. In the feature extraction phase, the signature image is partitioned into eight equally sized vertical grids and grid structured morphological pattern spectra for each grid is obtained. The grid structured morphological spectrum is represented in the form of 10-bin histogram and normalised to overcome the problem of scaling. The eighty dimensional feature vector is obtained by concatenating all the eight vertical morphological spectrum based normalised histogram. For verification purpose, we have considered two well known classifiers, namely SVM and MLP and conducted experiments on standard signature datasets namely CEDAR, GPDS-160 and MUKOS, a regional language (Kannada) dataset. The comparative study is also provided with the well known approaches to exhibit the performance of the proposed approach.
{"title":"Grid structured morphological pattern spectrum for off-line signature verification","authors":"B H Shekar, R. Bharathi, J. Kittler, Y. Vizilter, Leonid Mestestskiy","doi":"10.1109/ICB.2015.7139106","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139106","url":null,"abstract":"In this paper, we present a grid structured morphological pattern spectrum based approach for off-line signature verification. The proposed approach has three major phases: preprocessing, feature extraction and verification. In the feature extraction phase, the signature image is partitioned into eight equally sized vertical grids and grid structured morphological pattern spectra for each grid is obtained. The grid structured morphological spectrum is represented in the form of 10-bin histogram and normalised to overcome the problem of scaling. The eighty dimensional feature vector is obtained by concatenating all the eight vertical morphological spectrum based normalised histogram. For verification purpose, we have considered two well known classifiers, namely SVM and MLP and conducted experiments on standard signature datasets namely CEDAR, GPDS-160 and MUKOS, a regional language (Kannada) dataset. The comparative study is also provided with the well known approaches to exhibit the performance of the proposed approach.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131888091","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139111
Yinhang Tang, Xiang Sun, Di Huang, J. Morvan, Yunhong Wang, Liming Chen
The classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.
{"title":"3D face recognition with asymptotic cones based principal curvatures","authors":"Yinhang Tang, Xiang Sun, Di Huang, J. Morvan, Yunhong Wang, Liming Chen","doi":"10.1109/ICB.2015.7139111","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139111","url":null,"abstract":"The classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133856571","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139068
Takuhiro Kimura, Yasushi Makihara, D. Muramatsu, Y. Yagi
We constructed a large-scale multi-quality multi-modal biometric score database to advance studies on quality-dependent score-level fusion. In particular, we focused on single sensor-based multi-modal biometrics because of their advantages of simple system construction, low cost, and wide availability in real situations such as CCTV footage-based criminal investigation, unlike conventional individual sensor-based multi-modal biometrics that require multiple sensors. As for the modalities of multiple biometrics, we extracted gait, head, and the height biometrics from a single walking image sequence, and considered spatial resolution (SR) and temporal resolution (TR) as quality measures that simultaneously affect the scores of individual modalities. We then computed biometric scores of 1912 subjects under a total of 130 combinations of the quality measures, i.e., 13 SRs and 10 TRs, and constructed a very large-scale biometric score database composed of 1,814,488 genuine scores and 3,467,486,568 imposter scores. We finally provide performance evaluation results both for quality-independent and quality-dependent score-level fusion approaches using two protocols that will be beneficial to the score-level fusion research community.
{"title":"Single sensor-based multi-quality multi-modal biometric score database and its performance evaluation","authors":"Takuhiro Kimura, Yasushi Makihara, D. Muramatsu, Y. Yagi","doi":"10.1109/ICB.2015.7139068","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139068","url":null,"abstract":"We constructed a large-scale multi-quality multi-modal biometric score database to advance studies on quality-dependent score-level fusion. In particular, we focused on single sensor-based multi-modal biometrics because of their advantages of simple system construction, low cost, and wide availability in real situations such as CCTV footage-based criminal investigation, unlike conventional individual sensor-based multi-modal biometrics that require multiple sensors. As for the modalities of multiple biometrics, we extracted gait, head, and the height biometrics from a single walking image sequence, and considered spatial resolution (SR) and temporal resolution (TR) as quality measures that simultaneously affect the scores of individual modalities. We then computed biometric scores of 1912 subjects under a total of 130 combinations of the quality measures, i.e., 13 SRs and 10 TRs, and constructed a very large-scale biometric score database composed of 1,814,488 genuine scores and 3,467,486,568 imposter scores. We finally provide performance evaluation results both for quality-independent and quality-dependent score-level fusion approaches using two protocols that will be beneficial to the score-level fusion research community.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133581502","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139100
M. Sandhya, M. Prasad
In this paper we focus on constructing k-Nearest Neighborhood Structure(k - NNS) for minutiae points in a fingerprint image. For each minutiae point in a fingerprint, a k - NNS is constructed taking the local and global features of minutiae points. This structure is quantized and mapped onto a 2D array to generate a fixed length 1D bit-string. Further this bit string is applied with a DFT to generate a complex vector. Finally the complex vector is multiplied by a user specific random matrix to generate the cancelable template. We tested our proposed method on database FV C2002 and experimental results depicts the validity of the proposed method in terms of requirements of cancelable biometrics namely diversity, accuracy, irreversibility and revocability.
{"title":"k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection","authors":"M. Sandhya, M. Prasad","doi":"10.1109/ICB.2015.7139100","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139100","url":null,"abstract":"In this paper we focus on constructing k-Nearest Neighborhood Structure(k - NNS) for minutiae points in a fingerprint image. For each minutiae point in a fingerprint, a k - NNS is constructed taking the local and global features of minutiae points. This structure is quantized and mapped onto a 2D array to generate a fixed length 1D bit-string. Further this bit string is applied with a DFT to generate a complex vector. Finally the complex vector is multiplied by a user specific random matrix to generate the cancelable template. We tested our proposed method on database FV C2002 and experimental results depicts the validity of the proposed method in terms of requirements of cancelable biometrics namely diversity, accuracy, irreversibility and revocability.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123575256","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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