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Periocular Region

About: Periocular Region is a research topic. Over the lifetime, 256 publications have been published within this topic receiving 4424 citations.


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Proceedings ArticleDOI
01 Jan 2019
TL;DR: In this paper, the authors used probabilistic occlusion masking to gain insight on the discriminative power of the iris texture for gender prediction, and found that the gender related information is primarily in the periocular region.
Abstract: Predicting gender from iris images has been reported by several researchers as an application of machine learning in biometrics. Recent works on this topic have suggested that the preponderance of the gender cues is located in the periocular region rather than in the iris texture itself. This paper focuses on teasing out whether the information for gender prediction is in the texture of the iris stroma, the periocular region, or both. We present a larger dataset for gender from iris, and evaluate gender prediction accuracy using linear SVM and CNN, comparing hand-crafted and deep features. We use probabilistic occlusion masking to gain insight on the problem. Results suggest the discriminative power of the iris texture for gender is weaker than previously thought, and that the gender-related information is primarily in the periocular region.

14 citations

Book ChapterDOI
03 Dec 2011
TL;DR: The goal is to convey some of the difficulties in extracting the iris structure in images of the eye characterized by variations in illumination, eye-lid and eye-lash occlusion, defocus blur, motion blur, and low resolution.
Abstract: The face region immediately surrounding one, or both, eyes is called the periocular region. This paper presents an iris segmentation algorithm for challenging periocular images based on a novel iterative ray detection segmentation scheme. Our goal is to convey some of the difficulties in extracting the iris structure in images of the eye characterized by variations in illumination, eye-lid and eye-lash occlusion, defocus blur, motion blur, and low resolution. Experiments on the Face and Ocular Challenge Series (FOCS) database from the U.S. National Institute of Standards and Technology (NIST) emphasize the pros and cons of the proposed segmentation algorithm.

14 citations

Journal ArticleDOI
TL;DR: Pimecrolimus cream 1%, a nonsteroid, cell‐selective inhibitor of inflammatory‐cytokine release, is effective in the treatment of inflammatory skin diseases, such as chronic irritant dermatitis of the hands, and thus offers a potential therapeutic option for this indication.
Abstract: Background Irritant dermatitis of the face and neck is particularly prevalent in women ≥ 30 years old, who typically present with periocular cutaneous symptoms. Current therapies are limited, indicating a need for rapid, effective alternatives. Pimecrolimus cream 1%, a nonsteroid, cell-selective inhibitor of inflammatory-cytokine release, is effective in the treatment of inflammatory skin diseases, such as chronic irritant dermatitis of the hands, and thus offers a potential therapeutic option for this indication. This study reports on the safety and efficacy of pimecrolimus treatment in patients with irritant periocular dermatitis, extending to the face and neck in some patients. Methods Twenty-seven patients with periocular irritant dermatitis (extending onto the face and neck in eight) were treated twice daily with pimecrolimus cream 1% for 7 d, followed by once-daily application for a further 7 d. Erythema, swelling, and pruritus were assessed at baseline, weeks 1–4 using a 4-point clinical score (0, absent; 1, mild; 2, moderate; and 3, severe). Results All patients showed marked improvement within 2–3 d of treatment with disease clearance in 23 of 27 patients within 14 d. In the remaining four patients, mild relapse occurred at weeks 3–4, but improvement was observed following a further 10-d treatment. Side-effects were mild and transient. Conclusion Pimecrolimus cream 1% provides a new potential option for treatment of irritant dermatitis of the periocular region, head and neck. Further double-blind, controlled studies are required to confirm the efficacy and safety of pimecrolimus cream 1% for this indication.

14 citations

DOI
31 Aug 2014
TL;DR: The latest experiments have shown that a face recognition software recently developed in the research group can be adapted to perform cross spectral matching of partial face images and it is shown that three separate face regions such as eyes and nasal bridge, cheeks and nasal tip, and mouth and a part of the chin display similar matching performance.
Abstract: Matching partial heterogeneous face images to a gallery of visible images is a challenging research problem. This scenario is motivated by a number of surveillance applications such as recognition of subjects at night or in the presence of challenging environmental conditions. Standoff distances may range from a meter to hundred meters. Our latest experiments have shown that a face recognition software recently developed in our research group can be adapted to perform cross spectral matching of partial face images. The images are encoded with Gabor Generalized Local Binary Patterns and Gabor Weber operators and matched by means of a Kullbuck-Leibler metric. Our analysis has shown that three separate face regions such as (1) eyes and nasal bridge, (2) cheeks and nasal tip, and (3) mouth and a part of the chin display similar matching performance. Furthermore, we have evaluated performance of periocular regions. For a short standoff distance of 1.5 meters and a database of 48 classes, matching a Short Wave Infrared (SWIR) periocular region against visible regions resulted in 0.7 Genuine Accept Rate (GAR) at False Accept Rate (FAR) set to 0.01. For a long standoff distance of 106 meters and a database of 48 classes, matching SWIR against visible periocular regions yielded 0.4 GAR at FAR equal to 0.1. For a short standoff distance of 1.5 meters and a database of 200 classes, matching a Medium Wave Infrared (MWIR) periocular region against visible regions resulted in 0.35 GAR at FAR set to 0.1.

14 citations

Proceedings ArticleDOI
01 Oct 2019
TL;DR: It is proposed to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN and the detection is as accurate as performed separately, but with a lower computational cost.
Abstract: 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 community1. 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.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
202113
202032
201929
201815
201719