Method for using visible ocular vasculature for mobile biometrics

Vikas Gottemukkula; Sashi Saripalle; Sriram P. Tankasala; Reza Derakhshani

Method for using visible ocular vasculature for mobile biometrics

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Author(s): Vikas Gottemukkula ¹ ; Sashi Saripalle ² ; Sriram P. Tankasala ² ; Reza Derakhshani ²
- Affiliations: 1: EyeVerify, 1911 B West 45th Ave, Kansas City, USA ;
  2: Department of Computer Science and Electrical Engineering, University of Missouri at Kansas City, 5110 Rockhill Road, Kansas City, USA
Source: Volume 5, Issue 1, March 2016, p. 3 – 12
DOI: 10.1049/iet-bmt.2014.0059 , Print ISSN 2047-4938, Online ISSN 2047-4946

Received 03/11/2014, Accepted 04/10/2015, Revised 21/07/2015, Published 01/03/2016

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.

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