Robust Iris Localisation in Challenging Scenarios

Monteiro, João C.; Sequeira, Ana F.; Oliveira, Hélder; Cardoso, Jaime S.

doi:10.1007/978-3-662-45944-7_10

Cited by 6 publications

(6 citation statements)

References 26 publications

(26 reference statements)

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“…They first used a feature selection step on the features of the studied methods to obtain the "best features" and then used well-known classifiers for the decisionmaking. In addition, they applied iris segmentation [44] to obtaining the iris contour and adapted the feature extraction processes to the resulting non-circular iris regions. The validation considered five datasets (BioSec [25], [40], MobBIOfake [27], Warsaw [26], Clarkson [42] and NotreDame [45].…”

Section: Daugmanmentioning

confidence: 99%

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Menotti

Chiachia

Pinto

et al. 2015

IEEE Trans.Inform.Forensic Secur.

485

248

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Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.Comment: Pre-print of article that will appear in the IEEE Transactions on Information Forenseics and Security (T.IFS), Special Issue on Biometric Spoofing and Countermeasures, vol 10, n. 4, April 201

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Section: Daugmanmentioning

confidence: 99%

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Menotti

Chiachia

Pinto

et al. 2015

IEEE Trans.Inform.Forensic Secur.

485

248

View full text Add to dashboard Cite

show abstract

“…We participated in an iris liveness competition, the "LivDet Competition 2013" (Clarkson University and of Technology, 2013a), held as part of the IEEE BTAS 2013 1 . We applied this methodology combined with an automatic segmentation method (Monteiro et al, 2013;Monteiro et al, 2014) and achieved the first place 2 .…”

Section: Discussionmentioning

confidence: 99%

Iris Liveness Detection Methods in Mobile Applications

Sequeira

Murari

Cardoso

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Biometric systems are vulnerable to different kinds of attacks. Particularly, the systems based on iris are vulnerable to direct attacks consisting on the presentation of a fake iris to the sensor trying to access the system as it was from a legitimate user. The analysis of some countermeasures against this type of attacking scheme is the problem addressed in the present paper. Several state-of-the-art methods were implemented and included in a feature selection framework so as to determine the best cardinality and the best subset that conducts to the highest classification rate. Three different classifiers were used: Discriminant analysis, K nearest neighbours and Support Vector Machines. The implemented methods were tested in existing databases for iris liveness purposes (Biosec and Clarkson) and in a new fake database which was constructed for evaluation of iris liveness detection methods in the mobile scenario. The results suggest that this new database is more challenging than the others. Therefore, improvements are required in this line of research to achieve good performance in real world mobile applications.

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“…This database has already been tested in another work (Monteiro et al, 2014) concerning iris segmentation. Also, the iris image collection has allowed the construction of a dataset of fake images (MobBIOfake), composed by printed copies and their respective originals.…”

Section: Discussionmentioning

confidence: 99%

MobBIO: A Multimodal Database Captured with a Portable Handheld Device

Sequeira

Monteiro

Rebelo

et al. 2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Biometrics represents a return to a natural way of identification: testing someone by what (s)he is, instead of relying on something (s)he owns or knows seems likely to be the way forward. Biometric systems that include multiple sources of information are known as multimodal. Such systems are generally regarded as an alternative to fight a variety of problems all unimodal systems stumble upon. One of the main challenges found in the development of biometric recognition systems is the shortage of publicly available databases acquired under real unconstrained working conditions. Motivated by such need the MobBIO database was created using an Asus EeePad Transformer tablet, with mobile biometric systems in mind. The proposed database is composed by three modalities: iris, face and voice.

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Robust Iris Localisation in Challenging Scenarios

Cited by 6 publications

References 26 publications

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Iris Liveness Detection Methods in Mobile Applications

MobBIO: A Multimodal Database Captured with a Portable Handheld Device

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