Matching face against iris images using periocular information

Jillela, Raghavender; Ross, Arun

doi:10.1109/icip.2014.7026012

Cited by 27 publications

(20 citation statements)

References 19 publications

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“…In a more recent work, an EER of 1.26% and a value of 96.04% for Genuine Match Rate (GMR) at 0.01% False Match Rate (FMR) are obtained by using Binarized Statistical Image Features along with the χ 2 distance metric and simple fusion [26]. An even more challenging problem involves matching the ocular region of a face image obtained in the VIS spectrum with an iris image obtained in the NIR spectrum [15].…”

Section: Cross-spectral Recognitionmentioning

confidence: 99%

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Sequeira

Chen

Ferryman

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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This work presents the 2 nd Cross-Spectrum Iris/Periocular Recognition Competition (CrossEyed2017). 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.

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Section: Cross-spectral Recognitionmentioning

confidence: 99%

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Sequeira

Chen

Ferryman

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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“…The proposed framework can be applied to comparison of images from different spectra too [3]. In the context of smartphone recognition, where high resolution images are usual, fusion with the iris modality is another possibility [18].…”

Section: Discussionmentioning

confidence: 99%

“…surveillance or smartphones [2]. However, this massive availability of devices results in heterogeneous quality between probe and gallery images, which is known to reduce performance significantly when different capture devices are used [3]. Even if the sensors work in the same spectrum, they may have different spatial sampling rate, illumination sources, field of view, etc.…”

Section: Introductionmentioning

confidence: 99%

Log-likelihood score level fusion for improved cross-sensor smartphone periocular recognition

Alonso‐Fernandez

Raja

Busch

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The proliferation of cameras and personal devices results in a wide variability of imaging conditions, producing large intra-class variations and a significant performance drop when images from heterogeneous environments are compared. However, many applications require to deal with data from different sources regularly, thus needing to overcome these interoperability problems. Here, we employ fusion of several comparators to improve periocular performance when images from different smartphones are compared. We use a probabilistic fusion framework based on linear logistic regression, in which fused scores tend to be log-likelihood ratios, obtaining a reduction in cross-sensor EER of up to 40% due to the fusion. Our framework also provides an elegant and simple solution to handle signals from different devices, since same-sensor and crosssensor score distributions are aligned and mapped to a common probabilistic domain. This allows the use of Bayes thresholds for optimal decision making, eliminating the need of sensor-specific thresholds, which is essential in operational conditions because the threshold setting critically determines the accuracy of the authentication process in many applications.

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“…Another avenue of research, with some very recent research works, is the cross-spectral matching of NIR and VW images [27,28] or the extraction of features based on their suitability for individual periocular areas and/or illumination [29].…”

Section: Discussionmentioning

confidence: 99%

Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

Alonso‐Fernandez

Mikaelyan

Bigün

2015

3rd International Workshop on Biometrics and Forensics (IWBF 2015)

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Periocular refers to the facial region in the eye vicinity. It can be easily obtained with existing face and iris setups, and it appears in iris images, so its fusion with the iris texture has a potential to improve the overall recognition. It is also suggested that iris is more suited to near-infrared (NIR) illumination, whereas the periocular modality is best for visible (VW) illumination. Here, we evaluate three periocular and three iris matchers based on different features. As experimental data, we use five databases, three acquired with a close-up NIR camera, and two in VW light with a webcam and a digital camera. We observe that the iris matchers perform better than the periocular matchers with NIR data, and the opposite with VW data. However, in both cases, their fusion can provide additional performance improvements. This is specially relevant with VW data, where the iris matchers perform significantly worse (due to low resolution), but they are still able to complement the periocular modality.

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Matching face against iris images using periocular information

Cited by 27 publications

References 19 publications

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Log-likelihood score level fusion for improved cross-sensor smartphone periocular recognition

Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

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