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

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.