ICIP 2016 competition on mobile ocular biometric recognition

Rattani, Ajita; Derakhshani, Reza; Saripalle, Sashi K.; Gottemukkula, Vikas

doi:10.1109/icip.2016.7532371

Cited by 72 publications

(54 citation statements)

References 14 publications

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“…The seven publicly available databases utilized in this study are NIST-Multimodal database, NIST-Face database [23], IITDelhi Palmprint V1 database [24], IIT Delhi Ear Database [25], Hong Kong Polytechnic University Contactless Hand Dorsal Images database [26], Mobile Biometry (MOBIO) face database [27] and Visible Light Mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile database [28].…”

Section: Experimental Datamentioning

confidence: 99%

Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition

et al. 2020

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.

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Section: Experimental Datamentioning

confidence: 99%

Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition

et al. 2020

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“…We implemented the system using MATLAB R2015a on a PC with Intel (R) Core ™ i7-3610QM CPU @ 2.30 GHz and 12 GB RAM. The experiments were conducted on VISIT 1 of VISOB dataset [16], which is available in public domain. The identification performance of the system is reported in terms of accuracy, whereas the performance of verification is reported in terms of Genuine Match Rate (GMR) at False Acceptance Rate (FAR) =10 -2 and Equal Error Rate (EER).…”

Section: Evaluation Protocolmentioning

confidence: 99%

ConvSRC: SmartPhone-based periocular recognition using deep convolutional neural network and sparsity augmented collaborative representation

Alahmadi

Hussain

Aboalsamh

et al. 2020

IFS

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A B S T R A C TSmartphone based periocular recognition has gained significant attention from biometric research community because of the limitations of biometric modalities like face, iris etc. Most of the existing methods for periocular recognition employ hand-crafted features. Recently, learning based image representation techniques like deep Convolutional Neural Network (CNN) have shown outstanding performance in many visual recognition tasks. CNN needs a huge volume of data for its learning, but for periocular recognition only limited amount of data is available. The solution is to use CNN pre-trained on the dataset from the related domain, in this case the challenge is to extract efficiently the discriminative features. Using a pertained CNN model (VGG-Net), we propose a simple, efficient and compact image representation technique that takes into account the wealth of information and sparsity existing in the activations of the convolutional layers and employs principle component analysis. For recognition, we use an efficient and robust Sparse Augmented Collaborative Representation based Classification (SA-CRC) technique. For thorough evaluation of ConvSRC (the proposed system), experiments were carried out on the VISOB challenging database which was presented for periocular recognition competition in ICIP2016. The obtained results show the superiority of ConvSRC over the state-of-the-art methods; it obtains a GMR of more than 99% at FMR = 10 -3 and outperforms the first winner of ICIP2016 challenge by 10%.

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“…We employed the following public databases: CASIA-Iris-Interval [37], CASIA-Iris-Lamp [37], CASIA-Iris-Thousand [37], Cross-Eyed-VIS [52], CSIP [53], MICHE-I [54], MobBIO [55], NICE-II [56], PolyU-VIS [57], UBIRIS.v2 [56] and VISOB [58]. An overview of the important features of all databases used in this work can be seen in Table II.…”

Section: A Databasesmentioning

confidence: 99%

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations

Lucio

Laroca

Zanlorensi

et al. 2019

2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations can be used in recognition systems based on the iris and periocular regions, given the much smaller engineering effort required to manually annotate the training images. We manually made coarse annotations of the iris and periocular regions (≈ 122K images from the visible (VIS) spectrum and ≈ 38K images from the near-infrared (NIR) spectrum). The iris annotations in the NIR databases were generated semi-automatically by first applying an iris segmentation CNN and then performing a manual inspection. These annotations were made for 11 well-known public databases (3 NIR and 8 VIS) designed for the iris-based recognition problem, and are publicly available to the research community 1 . Experimenting our proposal on these databases, we highlight two results. First, the Faster R-CNN + Feature Pyramid Network (FPN) model reported an Intersection over Union (IoU) higher than YOLOv2 (91.86% vs 85.30%). Second, the detection of the iris and periocular regions being performed simultaneously is as accurate as performed separately, but with a lower computational cost, i.e., two tasks were carried out at the cost of one.

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ICIP 2016 competition on mobile ocular biometric recognition

Cited by 72 publications

References 14 publications

Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition

Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition

ConvSRC: SmartPhone-based periocular recognition using deep convolutional neural network and sparsity augmented collaborative representation

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations

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