Enhancement and Registration Schemes for Matching Conjunctival Vasculature

Abstract

Ocular biometrics has made significant strides over the past decade primarily due to the rapid advances in iris recognition. Recent literature has investigated the possibility of using conjunctival vasculature as an added ocular biometric. These patterns, observed on the sclera of the human eye, are especially significant when the iris is off-angle with respect to the acquisition device resulting in the exposure of the scleral surface. In this work, we design enhancement and registration methods to process and match conjunctival vasculature obtained under non-ideal conditions. The goal is to determine if conjunctival vasculature is a viable biometric in an operational environment. Initial results are promising and suggest the need for designing advanced image processing and registration schemes for furthering the utility of this novel biometric. However, we postulate that in an operational environment, conjunctival vasculature has to be used with the iris in a bimodal configuration.

Periocular Biometrics in the Visible Spectrum

“…3. While conjunctival vasculature can be imaged at a distance, the curvature of the sclera, the specular reflections in the image and the fineness of the vascular patterns, can confound the feature extraction and matching modules of the biometric system [6]. …”

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

“…Previously study is manual based segmentation, it has been applied by [4] and [5] the study is an unreliable approach for real-time applications because of the human supervision required with the segmentation process and the expensive processing time. In the semi-automated method suggested by [6], [7] and [8] based on K-means clustering, the eyelids included in the resulting sclera image were manually corrected. After that, two automated strategies were suggested based on sclera pixel thresholding and a sclera shape contour to extract sclera regions.…”

Survey Paper on Sclera Segmentations Techniques

“…After that, Derakhshani and Ross [11] investigated a new method for representing and matching the texture of blood vessels using wavelet-derived features and neural network classifiers. In [12], a semi-automated sclera segmentation scheme was used along with an image enhancement and registration scheme to process information in the blood veins of the sclera. Thomas et al [13] suggested a new automated method for sclera segmentation based on a single skinbased segmentation in the RGB color space.…”

“…the difference between near infrared and green pixel intensities is larger for the sclera region. In [21] a K-means clustering approach is employed to segment the sclera. A survey of the sclera recognition works until 2013 was made in [22] and with regards to sclera segmentation the survey shows that the few existing approaches are relying on various assumptions, e.g.…”

“…In [29], the authors have designed enhancement and registration methods to process and match conjunctival vasculature obtained under non-ideal conditions.…”