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Alonso-Fernandez, F., Farrugia, R. A., Bigun, J., Fierrez, J. & Gonzalez-Sosa, E. (2019). A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning. IEEE Access, 7, 6519-6544
Open this publication in new window or tab >>A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 6519-6544Article in journal (Refereed) Published
Abstract [en]

The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches need thus to incorporate specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an Eigen-patches reconstruction method based on PCA Eigentransformation of local image patches. The structure of the iris is exploited by building a patch-position dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is among the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators, which were used to carry out biometric verification and identification experiments. Experimental results show that the proposed method significantly outperforms both bilinear and bicubic interpolation at very low-resolution. The performance of a number of comparators attain an impressive Equal Error Rate as low as 5%, and a Top-1 accuracy of 77-84% when considering iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching. © 2018, Emerald Publishing Limited.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2019
Keywords
Iris hallucination, iris recognition, eigen-patch, super-resolution, PCA
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38659 (URN)10.1109/ACCESS.2018.2889395 (DOI)2-s2.0-85059007584 (Scopus ID)
Funder
Swedish Research Council, 2016-03497EU, FP7, Seventh Framework Programme, COST IC1106Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISRVINNOVA, 2018-00472
Available from: 2018-12-20 Created: 2018-12-20 Last updated: 2019-01-25Bibliographically approved
Krish, R. P., Fierrez, J., Ramos, D., Alonso-Fernandez, F. & Bigun, J. (2019). Improving Automated Latent Fingerprint Identification Using Extended Minutia Types. Information Fusion, 50, 9-19
Open this publication in new window or tab >>Improving Automated Latent Fingerprint Identification Using Extended Minutia Types
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2019 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 9-19Article in journal (Refereed) In press
Abstract [en]

Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2019
Keywords
Latent Fingerprints, Forensics, Extended Feature Sets, Rare minutiae features
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38113 (URN)10.1016/j.inffus.2018.10.001 (DOI)
Projects
BBfor2
Funder
EU, FP7, Seventh Framework Programme, FP7-ITN-238803Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISR
Note

R.K. was supported for the most part of this work by a Marie Curie Fellowship under project BBfor2 from European Commission (FP7-ITN-238803). This work has also been partially supported by Spanish Guardia Civil, and project CogniMetrics (TEC2015-70627-R) from Spanish MINECO/FEDER. The researchers from Halmstad University acknowledge funding from KK-SIDUS-AIR 485 project and the CAISR program in Sweden.

Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2019-01-02
Alonso-Fernandez, F., Bigun, J. & Englund, C. (2018). Expression Recognition Using the Periocular Region: A Feasibility Study. In: : . Paper presented at The 14th International Conference on Signal Image Technology & Internet Based Systems, SITIS 2018, Las Palmas de Gran Canaria, Spain, 26-29 November, 2018.
Open this publication in new window or tab >>Expression Recognition Using the Periocular Region: A Feasibility Study
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0%/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.

Keywords
Expression Recognition, Emotion Recognition, Periocular Analysis, Periocular Descriptor
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Identifiers
urn:nbn:se:hh:diva-38505 (URN)
Conference
The 14th International Conference on Signal Image Technology & Internet Based Systems, SITIS 2018, Las Palmas de Gran Canaria, Spain, 26-29 November, 2018
Projects
SIDUS-AIR
Funder
Swedish Research CouncilKnowledge Foundation
Note

Funding: Author F. A.-F. thanks the Swedish Research Council for funding his research. Authors acknowledge the CAISR program and the SIDUS-AIR project of the Swedish Knowledge Foundation.

Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-02-04Bibliographically approved
Hernandez-Diaz, K., Alonso-Fernandez, F. & Bigun, J. (2018). Periocular Recognition Using CNN Features Off-the-Shelf. In: : . Paper presented at International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, Sept. 26-29, 2018.
Open this publication in new window or tab >>Periocular Recognition Using CNN Features Off-the-Shelf
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these offthe-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to ~40%, with the fusion of CNN and traditional features providing additional improvements.

Keywords
Periocular recognition, deep learning, biometrics, Convolutional Neural Network
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-37704 (URN)
Conference
International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, Sept. 26-29, 2018
Projects
SIDUS-AIR
Funder
Knowledge Foundation, SIDUS-AIRSwedish Research Council, 2016-03497VINNOVA, 2018-00472Knowledge Foundation, CAISR
Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2018-10-02Bibliographically approved
Ranftl, A., Alonso-Fernandez, F., Karlsson, S. & Bigun, J. (2017). A Real-Time AdaBoost Cascade Face Tracker Based on Likelihood Map and Optical Flow. IET Biometrics, 6(6), 468-477
Open this publication in new window or tab >>A Real-Time AdaBoost Cascade Face Tracker Based on Likelihood Map and Optical Flow
2017 (English)In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 6, no 6, p. 468-477Article in journal (Refereed) Published
Abstract [en]

We present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola-Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola-Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. We also evaluate two recently published face detectors based on Convolutional Networks and Deformable Part Models, with our algorithm showing a comparable accuracy at a fraction of the computation time.

Place, publisher, year, edition, pages
Stevenage: The Institution of Engineering and Technology, 2017
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-33836 (URN)10.1049/iet-bmt.2016.0202 (DOI)000415218200012 ()
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, CAISRKnowledge Foundation, SIDUS-AIR
Available from: 2017-05-11 Created: 2017-05-11 Last updated: 2017-11-29Bibliographically approved
Alonso-Fernandez, F. & Bigun, J. (2017). An Overview of Periocular Biometrics. In: Christian Rathgeb & Christoph Busch (Ed.), Iris and Periocular Biometric Recognition: (pp. 29-53). London: The Institution of Engineering and Technology
Open this publication in new window or tab >>An Overview of Periocular Biometrics
2017 (English)In: Iris and Periocular Biometric Recognition / [ed] Christian Rathgeb & Christoph Busch, London: The Institution of Engineering and Technology , 2017, p. 29-53Chapter in book (Refereed)
Abstract [en]

Periocular biometrics specifically refers to the externally visible skin region of the face that surrounds the eye socket. Its utility is specially pronounced when the iris or the face cannot be properly acquired, being the ocular modality requiring the least constrained acquisition process. It appears over a wide range of distances, even under partial face occlusion (close distance) or low resolution iris (long distance), making it very suitable for unconstrained or uncooperative scenarios. It also avoids the need of iris segmentation, an issue in difficult images. In such situation, identifying a suspect where only the periocular region is visible is one of the toughest real-world challenges in biometrics. The richness of the periocular region in terms of identity is so high that the whole face can even be reconstructed only from images of the periocular region. The technological shift to mobile devices has also resulted in many identity-sensitive applications becoming prevalent on these devices.

Place, publisher, year, edition, pages
London: The Institution of Engineering and Technology, 2017
Series
IET security series ; 5
Keywords
face recognition, image reconstruction, unconstrained scenarios, eye socket, periocular biometrics, partial-face occlusion, face reconstruction, externally visible skin region, uncooperative scenarios, low-resolution iris
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-37705 (URN)978-1-78561-168-1 (ISBN)9781785611698 (ISBN)
Funder
Knowledge Foundation, CAISRSwedish Research Council, 2016-03497EU, FP7, Seventh Framework Programme, COST Action IC1106
Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2018-08-16Bibliographically approved
Alonso-Fernandez, F., Mikaelyan, A. & Bigun, J. (2017). Compact Multi-scale Periocular Recognition Using SAFE Features. In: Proceedings of the 23rd International Conference On Pattern Recognition (Icpr): . Paper presented at 23rd International Conference on Pattern Recognition (ICPR), Dec 4-8, 2016, Cancun, Mexico (pp. 1455-1460). IEEE Computer Society, Article ID 7899842.
Open this publication in new window or tab >>Compact Multi-scale Periocular Recognition Using SAFE Features
2017 (English)In: Proceedings of the 23rd International Conference On Pattern Recognition (Icpr), IEEE Computer Society, 2017, p. 1455-1460, article id 7899842Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes the presence of various symmetric curve families around image key points. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this unique point of the eye. As it is demonstrated, such discriminative properties can be encoded with a reduced set of symmetric curves. Experiments are done with a database of periocular images captured with a digital camera. We test our system against reference periocular features, achieving top performance with a considerably smaller feature vector (given by the use of a single key point). All the systems tested also show a nearly steady correlation between acquisition distance and performance, and they are also able to cope well when enrolment and test images are not captured at the same distance. Fusion experiments among the available systems are also provided. © 2016 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-35644 (URN)10.1109/ICPR.2016.7899842 (DOI)000406771301077 ()2-s2.0-85019080000 (Scopus ID)978-1-5090-4847-2 (ISBN)
Conference
23rd International Conference on Pattern Recognition (ICPR), Dec 4-8, 2016, Cancun, Mexico
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-01-13Bibliographically approved
Sequeira, A. F., Chen, L., Ferryman, J., Wild, P., Alonso-Fernandez, F., Bigun, J., . . . Kanhangad, V. (2017). Cross-Eyed 2017: Cross-Spectral Iris/Periocular Recognition Competition. In: : . Paper presented at IEEE/IAPR International Joint Conference on Biometrics, IJCB, Denver, Colorado, USA, October 1-4, 2017 (pp. 725-732). New York: IEEE
Open this publication in new window or tab >>Cross-Eyed 2017: Cross-Spectral Iris/Periocular Recognition Competition
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This work presents the 2nd Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward. © 2017 IEEE

Place, publisher, year, edition, pages
New York: IEEE, 2017
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-34688 (URN)10.1109/BTAS.2017.8272762 (DOI)000426973200088 ()2-s2.0-85046282830 (Scopus ID)978-1-5386-1124-1 (ISBN)978-1-5386-1125-8 (ISBN)
Conference
IEEE/IAPR International Joint Conference on Biometrics, IJCB, Denver, Colorado, USA, October 1-4, 2017
Projects
SIDUS-AIRFastPassPROTECTHFACESWAN
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISREU, FP7, Seventh Framework Programme, 284989 (BEAT)
Available from: 2017-08-08 Created: 2017-08-08 Last updated: 2018-06-04Bibliographically approved
Alonso-Fernandez, F., Farrugia, R. A. & Bigun, J. (2017). Improving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches. In: Arslan Brömme, Christoph Busch, Antitza Dantcheva, Christian Rathgeb & Andreas Uhl (Ed.), 2017 International Conference of the Biometrics Special Interest Group (BIOSIG): . Paper presented at 16th International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, September 20-22, 2017. Bonn: Gesellschaft für Informatik, P-270, Article ID 8053512.
Open this publication in new window or tab >>Improving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches
2017 (English)In: 2017 International Conference of the Biometrics Special Interest Group (BIOSIG) / [ed] Arslan Brömme, Christoph Busch, Antitza Dantcheva, Christian Rathgeb & Andreas Uhl, Bonn: Gesellschaft für Informatik, 2017, Vol. P-270, article id 8053512Conference paper, Published paper (Refereed)
Abstract [en]

Relaxed acquisition conditions in iris recognition systems have significant effects on the quality and resolution of acquired images, which can severely affect performance if not addressed properly. Here, we evaluate two trained super-resolution algorithms in the context of iris identification. They are based on reconstruction of local image patches, where each patch is reconstructed separately using its own optimal reconstruction function. We employ a database of 1,872 near-infrared iris images (with 163 different identities for identification experiments) and three iris comparators. The trained approaches are substantially superior to bilinear or bicubic interpolations, with one of the comparators providing a Rank-1 performance of ∼88% with images of only 15×15 pixels, and an identification rate of 95% with a hit list size of only 8 identities. © 2017 Gesellschaft fuer Informatik.

Place, publisher, year, edition, pages
Bonn: Gesellschaft für Informatik, 2017
Series
Lecture Notes in Informatics (LNI) - Proceedings, ISSN 1617-5468 ; P-270
Keywords
Iris, biometrics, super-resolution, low resolution
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-34738 (URN)10.23919/BIOSIG.2017.8053512 (DOI)2-s2.0-85034572701 (Scopus ID)978-3-88579-664-0 (ISBN)978-1-5386-0396-3 (ISBN)
Conference
16th International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, September 20-22, 2017
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISR
Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2017-12-11Bibliographically approved
Alonso-Fernandez, F., Farrugia, R. A. & Bigun, J. (2017). Iris Super-Resolution Using Iterative Neighbor Embedding. In: Lisa O’Conner (Ed.), 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: . Paper presented at International Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society Workshop on Biometrics, Hawaii Convention Center HI, USA, 21-26 Jul, 2017 (pp. 655-663). Los Alamitos: IEEE Computer Society
Open this publication in new window or tab >>Iris Super-Resolution Using Iterative Neighbor Embedding
2017 (English)In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2017, p. 655-663Conference paper, Published paper (Refereed)
Abstract [en]

Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low- and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process. © 2017 IEEE

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2017
Keywords
Iris recognition, Image reconstruction, Image resolution, Manifolds, Training, Databases, Iris
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-33864 (URN)10.1109/CVPRW.2017.94 (DOI)2-s2.0-85030244663 (Scopus ID)978-1-5386-0733-6 (ISBN)978-1-5386-0734-3 (ISBN)
Conference
International Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society Workshop on Biometrics, Hawaii Convention Center HI, USA, 21-26 Jul, 2017
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, SIDUSKnowledge Foundation, CAISR
Available from: 2017-05-18 Created: 2017-05-18 Last updated: 2017-12-14Bibliographically approved
Projects
Lip-motion, face and speech analysis in synergy, for human-machine interfaces [2008-03876_VR]; Halmstad UniversityScale, orientation and illumination invariant information encoding and decoding-- A study on invariant visual codes [2011-05819_VR]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4929-1262

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