Paper Info
Title
An End-to-End Autofocus Camera for Iris on the Move
Abstract
For distant iris recognition, a long focal length lens is generally used to ensure the resolution of iris images, which reduces the depth of field and leads to potential defocus blur. To accommodate users standing statically at different distances, it is necessary to control focus quickly and accurately. And for users in motion, it is also expected to acquire a sufficient amount of accurately focused iris images. In this paper, we introduced a novel rapid auto-focus camera for active refocusing of the iris area of the moving objects with a focus-tunable lens. Our end-to-end computational algorithm can predict the best focus position from one single blurred image and generate the proper lens diopter control signal automatically. This scene-based active manipulation method enables real-time focus tracking of the iris area of a moving object. We built a testing bench to collect real-world focal stacks for evaluation of the autofocus methods. Our camera has reached an autofocus speed of over 50 fps. The results demonstrate the advantages of our proposed camera for biometric perception in static and dynamic scenes. The code is available at https://github.com/Debatrix/AquulaCam.
Year
DOI
Venue
2021
10.1109/IJCB52358.2021.9484340
2021 IEEE International Joint Conference on Biometrics (IJCB)
Keywords
DocType
ISSN
end-to-end autofocus camera,distant iris recognition,long focal length lens,defocus blur,active refocusing,moving object,focus-tunable lens,end-to-end computational algorithm,focus position,single blurred image,scene-based active manipulation method,real-time focus tracking,autofocus methods,autofocus speed,static scenes,dynamic scenes,iris image resolution,rapid auto-focus camera,biometric perception
Conference
2474-9680
ISBN
Citations 
PageRank 
978-1-6654-3781-3
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Leyuan Wang100.34
Kunbo Zhang2142.24
Yunlong Wang301.01
Zhenan Sun42379139.49