OCULAR

Ocular Biometrics in Unconstrained Sensing Environments

OCULAR
Project start:
1 January 2017
Project end:
30 December 2020
More info (PDF):
[[media: | pdf]]
Contact:
[[Fernando Alonso-Fernandez]]
Application Area:
[[Biometrics]]
Involved internal personnel
Involved external personnel
Involved partners
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Abstract

This is a four-years project financed the Swedish Research Council. The project is concerned with ocular biometrics in unconstrained sensing environments. Attention will be paid to the periocular modality (the part of the face surrounding the eye), which has shown a surprisingly high discrimination ability, and is the facial-ocular modality requiring the least constrained acquisition.

One goal is to contribute with methods for efficient ocular detection and segmentation. This is still a challenge, with most works relying on manual image annotation, or on detecting the full face, which may not be reliable for example under occlusion. We will continue initiated work with symmetry filters, and will explore deep learning algorithms too, which are giving promising results in many computer vision tasks. Low resolution is another limitation. Thus, another goal will be super-resolution (SR) reconstruction of ocular images. With few works focused on iris, and none on periocular, adaptation of the many available SR methods to the particularities of ocular images is a promising avenue yet to be explored.

Ubiquitous biometrics has emerged as critical not only in light of current security threats (e.g. identifying terrorists in surveillance videos), but also due to the proliferation of consumer electronics (e.g. smartphones) in need of continuous personal authentication for a wide variety of applications. By our contributions, we expect to be able to handle a wide range of variations in biometric imaging from these scenarios.

Ocular Biometrics in Unconstrained Sensing Environments

Swedish Research Council, Research Project No: 2016-03497

Iris Quality Measures

Several quality measures of the iris region have been implemented and evaluated, both locally (iris boundaries) and globally (whole eye region), including the proposal of novel algorithms. We have aimed to quantify image properties reported in the literature as having the greatest influence in iris recognition accuracy, in support of the standard ISO/IEC 29794-6 Biometric Sample Quality (part 6: Iris image). The algorithms implemented include measurements of: defocus blur (1 algorithm), motion blur and image interlace (1 algorithm), contrast of iris boundaries (2 algorithms), circularity of iris boundaries (1 algorithm), gray scale spread (2 algorithms), and occlusion (1 algorithm).