Method for using visible ocular vasculature for mobile biometrics

Abstract

Securing personal information on handheld devices, especially smartphones, has gained a significant interest in recent years. Yet, most of the popular biometric modalities require additional hardware. To overcome this difficulty, the authors propose utilising the existing visible light cameras in mobile devices. Leveraging visible vascular patterns on whites of the eye, they develop a method for biometric authentication suitable for smartphones. They start their process by imaging and segmenting whites of the eyes, followed by image quality assessment. The authors' stage 1 matcher is a three-step process that entails extracting interest points [Harris-Stephens, features from accelerated segment test, and speeded up robust features (SURF)], building features (SURF and fast retina keypoint) around those points, and match score generation using random sample consensus-based registration. Stage 2 matcher uses registered Gabor phase filtered images to generate orientation of local binary pattern features for its correlation-based match metric. A fusion of stage 1 and stage 2 match scores is calculated for the final decision. Using a dataset of 226 users, the authors' results show equal error rates as low as 0.04% for long-term verification tests. The success of their framework is further validated on UBIRIS v1 database.

Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions

“…Alkassar et al [3] proposed newly occluded eye detection for sclera validation along with sclera shape contour extraction. Gottemukkula et al [11] developed a method for an ocular biometric which is suitable for smartphones. Finally, two competitions for the sclera segmentation were organised by Das et al [12,13] resulting in using a neural network classifier to overcome some of noise factors.…”

“…iv. Ocular vasculature using smartphone proposed by Gottemukkula et al [11] has used only one mobile device and the dataset acquisition was indoor inside a lab environment with controlled illumination conditions and capturing distance. • In our previous work [17], we proposed a new method for sclera recognition when images are captured at-a-distance and on-themove.…”

“…Gottemukkula et al [11] developed a method for an ocular biometric which is suitable for smartphones.…”

“…Gottemukkula et al . [11] developed a method for an ocular biometric which is suitable for smartphones. Finally, two competitions for the sclera segmentation were organised by Das et al .…”

“…In addition, the quality measure involves discussing the occlusion cases and counting extracted features which not necessarily measure the image quality rather than feature extraction method quality. Ocular vasculature using smartphone proposed by Gottemukkula et al . [11] has used only one mobile device and the dataset acquisition was indoor inside a lab environment with controlled illumination conditions and capturing distance.…”