An End-to-End Autofocus Camera for Iris on the Move

TL;DR

This paper presents a rapid, end-to-end autofocus camera system for iris recognition on moving subjects, utilizing a focus-tunable lens and a computational algorithm to achieve real-time focus tracking at over 50 fps.

Contribution

It introduces a novel scene-based autofocus method with a focus-tunable lens and a predictive algorithm for real-time iris focus on moving objects.

Findings

Achieved autofocus speed over 50 fps.

Demonstrated effective focus tracking in static and dynamic scenes.

Validated the system with real-world focal stack data.

Abstract

For distant iris recognition, a long focal length lens is generally used to ensure the resolution ofiris images, which reduces the depth of field and leads to potential defocus blur. To accommodate users at different distances, it is necessary to control focus quickly and accurately. While for users in motion, it is expected to maintain the correct focus on the iris area continuously. In this paper, we introduced a novel rapid autofocus camera for active refocusing ofthe iris area ofthe moving objects using a focus-tunable lens. Our end-to-end computational algorithm can predict the best focus position from one single blurred image and generate a lens diopter control signal automatically. This scene-based active manipulation method enables real-time focus tracking of the iris area ofa 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 ofover 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.