Test of the Practicality and Feasibility of EDoF-Empowered Image Sensors for Long-Range Biometrics.

Sheng-Hsun Hsieh,Yung-Hui Li,Chung-Hao Tien

doi:10.3390/s16121994

Abstract

For many practical applications of image sensors, how to extend the depth-of-field (DoF) is an important research topic; if successfully implemented, it could be beneficial in various applications, from photography to biometrics. In this work, we want to examine the feasibility and practicability of a well-known “extended DoF” (EDoF) technique, or “wavefront coding,” by building real-time long-range iris recognition and performing large-scale iris recognition. The key to the success of long-range iris recognition includes long DoF and image quality invariance toward various object distance, which is strict and harsh enough to test the practicality and feasibility of EDoF-empowered image sensors. Besides image sensor modification, we also explored the possibility of varying enrollment/testing pairs. With 512 iris images from 32 Asian people as the database, 400-mm focal length and F/6.3 optics over 3 m working distance, our results prove that a sophisticated coding design scheme plus homogeneous enrollment/testing setups can effectively overcome the blurring caused by phase modulation and omit Wiener-based restoration. In our experiments, which are based on 3328 iris images in total, the EDoF factor can achieve a result 3.71 times better than the original system without a loss of recognition accuracy.

Highlights

Biometric recognition has been applied to many practical uses, including homeland security, e-commerce or other authentication management purposes
If we consider the power of the extended depth of field (EDoF) capability as one of the core objective functions from the experimental results, Approach 4 is the best approach with a 3.71 EDoF factor
We examine a number of EDoF approaches for the purpose of a distant iris recognition system

Summary

Introduction

Biometric recognition has been applied to many practical uses, including homeland security, e-commerce or other authentication management purposes. The personal attributes used for authentication were classified into two parts: (1) physiological attributes, such as DNA, facial features, retinal vasculature, fingerprint, hand geometry, iris texture and so on; and (2) individual behavior features, such as signature, keystroke, voice, and gait style [1]. Among these features, iris texture is one of the most attractive modalities because of its inherent distinctiveness, high stability over time and low risk of circumvention [2]. The iris images are normalized by transforming the coordinates from Cartesian to Polar

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Methods

Discussion

Conclusion