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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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Book ChapterDOI
18 Dec 2019
TL;DR: This research work is novel in the prospect that this is the first study for periocular recognition applying CNN-based super-resolution, and the off-the-shelf CNN features used in the work give improved rank-1 accuracy.
Abstract: A major challenge of the prevailing biometric systems is the short range of trait capture. Relaxing the range constraints imposed on the subjects arouses advanced challenges in the quality of image and resolution. In such scenarios, the lack of quality of acquired biometric information can be addressed using super-resolution, a technique of generating high-resolution images from low resolution counterparts. Procurement of the periocular images requires less cooperation of subjects compared to other ocular biometrics, thereby emerging as a reliable trait for unconstrained biometrics. For our work, UBIRIS v.2 is used which provides the relevant data for less constrained biometrics. The best rank-1 accuracy reported for periocular recognition with this database is 87.62%, and such works rely on the local and global feature descriptors. On this account, we cannot possibly arrive at the conclusion that these attributes in the literature are the best descriptors for the periocular region. One possible solution to achieve better recognition performance is to employ the latest trends in deep learning. Accordingly, we propose to apply deep learning-based super-resolution technique to the periocular images for improved identification efficiency. Our research work is novel in the prospect that this is the first study for periocular recognition applying CNN-based super-resolution. The off-the-shelf CNN features used in our work give improved rank-1 accuracy of 91.47%.

5 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an approach to extract optimum size periocular ROIs of two different shapes (polygon and rectangular) by using five reference points (inner and outer canthus points, two end points and the midpoint of eyebrow).
Abstract: With the onset of COVID-19 pandemic, wearing of face mask became essential and the face occlusion created by the masks deteriorated the performance of the face biometric systems. In this situation, the use of periocular region (region around the eye) as a biometric trait for authentication is gaining attention since it is the most visible region when masks are used. One important issue in periocular biometrics is the identification of an optimal size periocular ROI which contains enough features for authentication. The state of the art ROI extraction algorithms use fixed size rectangular ROI calculated based on some reference points like center of the iris or centre of the eye without considering the shape of the periocular region of an individual. This paper proposes a novel approach to extract optimum size periocular ROIs of two different shapes (polygon and rectangular) by using five reference points (inner and outer canthus points, two end points and the midpoint of eyebrow) in order to accommodate the complete shape of the periocular region of an individual. The performance analysis on UBIPr database using CNN models validated the fact that both the proposed ROIs contain enough information to identify a person wearing face mask.

5 citations

Journal ArticleDOI
TL;DR: This review focuses on the key prognostic factors and management considerations for patients with periocular BCC, and particular focus upon challenging cases and areas of controversy, drawing on the latest literature.
Abstract: Introduction: Basal cell carcinoma (BCC) is the commonest cancer in the United Kingdom, and accounts for over 90% of eyelid malignancies. The periocular region is considered a high-risk area requiring specialist management. This review focuses on the key prognostic factors and management considerations for patients with periocular BCC.Areas covered: The presentation, investigation and management priorities for patients with periocular BCC are discussed. Particular focus upon challenging cases and areas of controversy, drawing on the latest literature.Expert commentary: We recommend that all patients with suspected periocular BCC be referred for a specialist opinion, and in most cases undergo biopsy to determine histological subtype and guide management. Post-operative margin control with 3 mm surgical margins are recommended for primary, low-risk BCCs, and these patients may not require follow-up if excision is complete. 2 mm margins may be considered for small, well-circumscribed BCCs close to cr...

5 citations

Proceedings ArticleDOI
01 Jan 2014
TL;DR: This paper proposes an efficient face recognition system which is invariant to aging and makes use of the periocular region of an individual, which has been tested on a publicly available, FGNET database and self scanned and created Browns database.
Abstract: This paper proposes an efficient face recognition system which is invariant to aging. It makes use of the periocular region of an individual. Local Binary Patterns are used to extract these local features from the enhanced periocular region. Chi square distance is used to compute similarity between two facial images. It has been tested on a publicly available, FGNET database and self scanned and created Browns database.

5 citations

Proceedings ArticleDOI
TL;DR: The presence of topical cosmetics is shown to negatively impact the authentic distribution of iris match scores, causing an increase in the false non-match rate at a fixed false match rate.
Abstract: Iris biometrics systems rely on analysis of a visual presentation of the human iris, which must be extracted from the periocular region. Topical cosmetics can greatly alter the appearance of the periocular region, and can occlude portions of the iris texture. In this paper, the presence of topical cosmetics is shown to negatively impact the authentic distribution of iris match scores, causing an increase in the false non-match rate at a fixed false match rate.

5 citations


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