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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
Hernandez-Diaz, K., Alonso-Fernandez, F. & Bigun, J. (2019). Cross Spectral Periocular Matching using ResNet Features. In: : . Paper presented at 12th IAPR International Conference on Biometrics, Crete, Greece, June 4-7, 2019.
Open this publication in new window or tab >>Cross Spectral Periocular Matching using ResNet Features
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than other ocular modalities. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ 2 distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies.

National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-40499 (URN)
Conference
12th IAPR International Conference on Biometrics, Crete, Greece, June 4-7, 2019
Funder
Swedish Research Council, 2016-03497Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISR
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-10-11
Hernandez-Diaz, K., Alonso-Fernandez, F. & Bigun, J. (2019). Cross-Spectral Biometric Recognition with Pretrained CNNs as Generic Feature Extractors. In: : . Paper presented at Swedish Symposium on Image Analysis, SSBA, Gothenburg, Sweden, March 19-20, 2019.
Open this publication in new window or tab >>Cross-Spectral Biometric Recognition with Pretrained CNNs as Generic Feature Extractors
2019 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than face or iris. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ 2 distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies.

National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-40625 (URN)
Conference
Swedish Symposium on Image Analysis, SSBA, Gothenburg, Sweden, March 19-20, 2019
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-10-11
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) Published
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. © 2018 Elsevier B.V.

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)2-s2.0-85054739072 (Scopus ID)
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: 2019-04-10Bibliographically approved
Alonso-Fernandez, F., Farrugia, R. A., Fierrez, J. & Bigun, J. (2019). Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris. In: Ajita Rattani, Arun Ross (Ed.), Selfie Biometrics: . Springer
Open this publication in new window or tab >>Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris
2019 (English)In: Selfie Biometrics / [ed] Ajita Rattani, Arun Ross, Springer, 2019Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer, 2019
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38508 (URN)
Projects
SIDUS-AIR
Funder
Swedish Research CouncilVinnovaKnowledge Foundation
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-03-22
Alonso-Fernandez, F., Bigun, J. & Englund, C. (2018). Expression Recognition Using the Periocular Region: A Feasibility Study. In: Gabriella Sanniti di Baja, Luigi Gallo, Kokou Yetongnon, Albert Dipanda, Modesto Castrillón-Santana & Richard Chbeir (Ed.), 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS): . 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 (pp. 536-541). Los Alamitos: IEEE Computer Society
Open this publication in new window or tab >>Expression Recognition Using the Periocular Region: A Feasibility Study
2018 (English)In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [ed] Gabriella Sanniti di Baja, Luigi Gallo, Kokou Yetongnon, Albert Dipanda, Modesto Castrillón-Santana & Richard Chbeir, Los Alamitos: IEEE Computer Society, 2018, p. 536-541Conference 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.

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2018
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)978-1-5386-9385-8 (ISBN)978-1-5386-9386-5 (ISBN)
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: 2019-05-16Bibliographically approved
Hernandez-Diaz, K., Alonso-Fernandez, F. & Bigun, J. (2018). Periocular Recognition Using CNN Features Off-the-Shelf. In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG): . Paper presented at International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, Sept. 26-29, 2018. Piscataway, N.J.: IEEE
Open this publication in new window or tab >>Periocular Recognition Using CNN Features Off-the-Shelf
2018 (English)In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), Piscataway, N.J.: IEEE, 2018Conference 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.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE, 2018
Series
2018 International Conference of the Biometrics Special Interest Group (BIOSIG), ISSN 1617-5468 ; 2018
Keywords
Periocular recognition, deep learning, biometrics, Convolutional Neural Network
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-37704 (URN)10.23919/BIOSIG.2018.8553348 (DOI)978-3-88579-676-3 (ISBN)
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: 2019-05-23Bibliographically 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: : . Paper presented at Swedish Symposium on Image Analysis, SSBA, Linköping, Sweden, March 13-15, 2017.
Open this publication in new window or tab >>Compact Multi-scale Periocular Recognition Using SAFE Features
2017 (English)Conference 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.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-35644 (URN)
Conference
Swedish Symposium on Image Analysis, SSBA, Linköping, Sweden, March 13-15, 2017
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2019-09-26Bibliographically 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 UniversityFacial detection and recognition resilient to physical image deformations [2012-04313_VR]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4929-1262

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