Periocular Region

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

Periocular biometrics in mobile environment

Abstract: In this work we study periocular biometrics in a challenging scenario: a mobile environment, where person recognition can take place on a mobile device. The proposed technique, that models session variability, is evaluated for the authentication task on the MOBIO database, previously used in face recognition, and on a novel mobile biometric database named the CPqD Biometric Database, as well as compared to prior work. We show that in this particular mobile environment the periocular region is complementary to face recognition, but not superior, unlike shown in a previous study on a more controlled environment. We show also that a combination with face recognition brings a relative improvement of 7.93% in terms of HTER. Finally, the results of this paper are reproducible using an open software and a novel Web platform.

Person recognition based on fusion of iris and periocular biometrics

Abstract: In this paper we proposed a novel multimodal biometric approach using iris and periocular biometrics to improve the performance of iris recognition in case of non-ideal iris images. Though iris recognition has the highest accuracy among all the available biometrics, still the noises at the image acquisition stage degrade the recognition accuracy. The periocular region can act as a supporting biometric, in case the iris is obstructed by several noises. The periocular region is the part of the face immediately surrounding the eye. The approach is based on fusion of features of iris and periocular region. The approach has shown significant improvement in the performance of iris recognition. The evaluation was done on a test database created from the images of UBIRIS V2 and CASIA iris interval database. We achieved identification accuracy upto 96 % on the test database.

Oculoplastic aspects of ocular oncology.

Abstract: It is estimated that 5–10% of all cutaneous malignancies involve the periocular region and management of periocular skin cancers account for a significant proportion of the oculoplastic surgeon's workload. Epithelial tumours are most frequently encountered, including basal cell carcinoma, squamous cell carcinoma, and sebaceous gland carcinoma, in decreasing order of frequency. Non-epithelial tumours, such as cutaneous melanoma and Merkel cell carcinoma, rarely involve the ocular adnexae. Although non-surgical treatments for periocular malignancies are gaining in popularity, surgery remains the main treatment modality and has as its main aims tumour clearance, restoration of the eyelid function, protection of the ocular surface, and achieving a good cosmetic outcome. The purpose of this article is to review the management of malignant periocular tumours, with particular emphasis on surgical management.

Merkel cell carcinoma of the eyelid and periocular region.

Abstract: Merkel cell carcinoma (MCC) in the eyelid and periocular region can be treated surgically, in most cases, with preservation of the eye and reasonable visual function. Adjuvant radiation therapy, sentinel lymph node biopsy, and chemotherapy should be considered for MCC of the eyelid and periocular region, especially for larger tumors that are T2b or more advanced and lesions that present with regional nodal or distant metastasis.

Periocular biometric recognition using image sets

Abstract: Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification. Current techniques have focused on choosing a single best frame, mostly manually, for matching. In contrast, we formulate, for the first time, person identification based on periocular regions as an image set classification problem. We generate periocular region image sets from the Multi Bio-metric Grand Challenge (MBGC) NIR videos. Periocular regions of the right eyes are mirrored and combined with those of the left eyes to form an image set. Each image set contains periocular regions of a single subject. For imageset classification, we use six state-of-the-art techniques and report their comparative recognition and verification performances. Our results show that image sets of periocular regions achieve significantly higher recognition rates than currently reported in the literature for the same database.