Ocular biometrics: A survey of modalities and fusion approaches

Nigam, Ishan; Vatsa, Mayank; Singh, Richa

doi:10.1016/j.inffus.2015.03.005

Cited by 122 publications

(62 citation statements)

References 196 publications

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“…Fusion of face and ocular regions is not a new idea, as already pointed out in the recognition problem by [24] and [31]. Here we go further combining multi-scale information as we fuse, facial (F), head and shoulders (HS) and periocular (P) information for GC, see Figure 9.…”

Section: Multi-scale Score Level Fusionmentioning

confidence: 97%

“…The recent survey on ocular biometrics by [31] reviews the results achieved making use of three different modalities: iris, periocular and retina. Among them, we refer below to the first two, the external ones.…”

Section: Introductionmentioning

confidence: 99%

“…The mentioned survey by [31] also describes the advantages of modalities fusion. Indeed, the fusion of facial and periocular information improves identity recognition performance, proving the benefits in surveillance scenarios, where noise and noncooperation excludes the use of other ocular cues.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On using periocular biometric for gender classification in the wild

Castrillón-Santana

Lorenzo-Navarro

Ramón-Balmaseda

2016

Pattern Recognition Letters

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Section: Multi-scale Score Level Fusionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On using periocular biometric for gender classification in the wild

Castrillón-Santana

Lorenzo-Navarro

Ramón-Balmaseda

2016

Pattern Recognition Letters

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“…Furthermore, the identification using the iris region is non-invasive, that is, there is no need for physical contact to obtain and analyze an iris image [11]. However, after decades of research in personal identification, it has been observed that better results can be achieved by combining different biometric modalities [6], [12], [13]. A good example of it is the combination of iris and periocular-based biometrics [14], [15].…”

Section: Introductionmentioning

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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“…The periocular region, the area surrounding the eye, has shown a surprisingly high discrimination ability, while requiring the least constrained acquisition among ocular or facial modalities [1]. It has thus become a very popular modality due to the proliferation of unconstrained or uncooperative scenarios, e.g.…”

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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Ocular biometrics: A survey of modalities and fusion approaches

Cited by 122 publications

References 196 publications

On using periocular biometric for gender classification in the wild

On using periocular biometric for gender classification in the wild

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations

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

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