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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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TL;DR: In this paper, the location of the most relevant features that describe gender in periocular NIR images and evaluate its influence on its classification was analyzed and demonstrated, and it was shown that the perocular region contains more gender information than the iris region.
Abstract: Most gender classifications methods from NIR images have used iris information. Recent work has explored the use of the whole periocular iris region which has surprisingly achieve better results. This suggests the most relevant information for gender classification is not located in the iris as expected. In this work, we analyze and demonstrate the location of the most relevant features that describe gender in periocular NIR images and evaluate its influence its classification. Experiments show that the periocular region contains more gender information than the iris region. We extracted several features (intensity, texture, and shape) and classified them according to its relevance using the XgBoost algorithm. Support Vector Machine and nine ensemble classifiers were used for testing gender accuracy when using the most relevant features. The best classification results were obtained when 4,000 features located on the periocular region were used (89.22\%). Additional experiments with the full periocular iris images versus the iris-Occluded images were performed. The gender classification rates obtained were 84.35\% and 85.75\% respectively. We also contribute to the state of the art with a new database (UNAB-Gender). From results, we suggest focussing only on the surrounding area of the iris. This allows us to realize a faster classification of gender from NIR periocular images.

5 citations

Journal ArticleDOI
TL;DR: A thoughtful preoperative evaluation, conscientious patient selection, comprehensive informed consent, adequate training, and a cautious and conservative approach are reinforced to minimize moderate or even severe aesthetic and functional periocular complications.
Abstract: Laser periocular surgery has achieved an increased popularity, particularly since the widespread use of CO(2) and erbium:yttrium aluminum garnet laser and more recently with the development of nonablative laser technology. The main target of these techniques is to treat photoaging changes to obtain a rejuvenated skin. Despite the relatively safety of these procedures on experienced hands, postoperative complications affecting the periocular region, and the eye itself, may follow laser surgery. More common complications include persistent erythema, hyper- and hypopigmentation, and hypertrophic scarring. Viral, bacterial, or fungal skin infections may also jeopardize the postoperative period after periocular laser treatment. Severe burns, transitory or permanent lower lid ectropion, and even corneal injuries or ocular perforation are among the most severe hazards. The majority of these complications are related to the use of ablative technologies. A thoughtful preoperative evaluation, conscientious patient selection, comprehensive informed consent, adequate training, and a cautious and conservative approach are reinforced to minimize moderate or even severe aesthetic and functional periocular complications. Nonablative laser therapies are notably safer; however, their ability to significantly improve photoaged skin characteristics is still limited.

5 citations

Proceedings ArticleDOI
01 Aug 2020
TL;DR: This work explores a complementary approach to biometric face recognition, that is the dynamics of facial landmark during phonation while pronouncing sentences, which can be seen as a kind of signature that uniquely and unmistakably identifies an individual.
Abstract: Face identification is one of the most widely adopted approach in several real scenarios. Unfortunately, in presence of occlusions or non-cooperative subjects nontrivial issues arise. This work explores a complementary approach to biometric face recognition, that is the dynamics of facial landmark during phonation while pronouncing sentences. These dynamics (both periocular and labial area) can be seen as a kind of signature that uniquely and unmistakably identifies an individual. The experimental results focus the attention on periocular area, which represents one of the most discriminating physiological characteristics after the whole face. Preliminary experimental results conducted on a public dataset and considering 14 periocular features showed an accuracy slightly below 80%, which confirms the robustness in uncontrolled scenarios.

5 citations

Posted Content
TL;DR: In this article, 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.

5 citations

Book ChapterDOI
01 Jan 2017
TL;DR: This chapter presents an extensive study on periocular region based person identification using videos in the visible spectrum, near IR range, and also by using the hyperspectral image cubes in a relatively wider bandwidth and observes that the proposed two stage fusion is superior to single stage fusion.
Abstract: The performance of automatic person identification based on visual appearance significantly suffers under occlusions in many real life situations. These occlusions may be unintentional due to the use of different items such as head gear, headphone, head scarf, or may also be caused by the style of clothing or just hair style. Intentional facial occlusions occur when a particular person try to hide his identity by hiding his face and appearance from the security cameras. In many incidents captured by surveillance videos, it has been observed that the offenders have covered their appearance and faces from the camera, leaving only the small region around the eyes known as “periocular region.” It is because the periocular region cannot be covered to maintain proper vision. In this chapter we present an extensive study on periocular region based person identification using videos in the visible spectrum, near IR range, and also by using the hyperspectral image cubes in a relatively wider bandwidth. While most of the existing techniques for periocular recognition from videos have handpicked a single best frame from videos, we formulate periocular region based person identification in video as an image-set classification problem. For thorough analysis, we perform experiments on periocular regions extracted automatically from RGB videos, NIR videos, and hyperspectral image cubes. Each image-set is represented by four heterogeneous feature types and classified with six state-of-the-art image-set classification algorithms. We will discuss in detail our novel two stage inverse Error Weighted Fusion algorithm for feature and classifier score fusion. We observe that the proposed two stage fusion is superior to single stage fusion. Comprehensive experimental results are presented on four publicly available datasets including Multiple Biometric Grand Challenge (MBGC) NIR, MBGC visible spectrum dataset both by NIST, Carnegie Mellon University (CMU) Hyperspectral face database (Tech. Report CMURI-TR-02-25), and University of Beira Interior Periocular (UBIPr) dataset. In these experiments excellent recognition on all of the four datasets has been observed. These results are significantly better than the result of most of the existing state-of-the-art methods on the same datasets and under similar experimental setup. In addition to these improvements, we demonstrate the feasibility of image-set based periocular biometrics for real world applications. Deployment of security systems with periocular region based person identification algorithm will reduce the vulnerability of security systems to be hacked by non-cooperative individuals.

4 citations


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