All-in-Focus Iris Camera With a Great Capture Volume

Zhang, Kunbo; Shen, Zhenteng; Wang, Yunlong; Sun, Zhenan

doi:10.1109/ijcb48548.2020.9304932

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…In addition, capturing the region of interest (ROI) a whole face reduces the load on communication and processing speeds, and can improve the frame rate while keeping the resolution high. In systems that capture the iris of a moving person, such as iris at a distance (IAAD) and iris on the move (IOTM) [4,5,6], capturing partial face images at a high frame rate increases the chance of iris imaging with a moving subject.…”

Section: Introductionmentioning

confidence: 99%

Fast Eye Detector Using Siamese Network for NIR Partial Face Images

Ogino¹,

Shoji²,

Toizumi³

et al. 2022

Preprint

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This paper proposes a fast eye detection method based on fully-convolutional Siamese networks for iris recognition. The iris on the move system requires to capture high resolution iris images from a moving subject for iris recognition. Therefore, capturing images contains both eyes at high-frame-rate increases the chance of iris imaging. In order to output the authentication result in real time, the system requires a fast eye detector extracting the left and right eye regions from the image. Our method extracts features of a partial face image and a reference eye image using Siamese network frameworks. Similarity heat maps of both eyes are created by calculating the spatial cosine similarity between extracted features. Besides, we use CosFace as a loss function for training to discriminate the left and right eyes with high accuracy even with a shallow network. Experimental results show that our method trained by CosFace is fast and accurate compared with conventional generic object detection methods.

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Section: Introductionmentioning

confidence: 99%

Fast Eye Detector Using Siamese Network for NIR Partial Face Images

Ogino¹,

Shoji²,

Toizumi³

et al. 2022

Preprint

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“…Defocus blurring is one of the essential quality impacts in iris recognition for long-range uncontrolled objects [15,16,28]. The lens of an iris imaging system with a long focal distance is introduced to meet the high-resolution requirement of an iris image, which sacrifices the field of depth (DoF) [22,23,33]. Uncontrolled subjects can quickly leave the range of DoF, making it difficult to acquire clear iris images.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Zhang²,

Wang³

et al. 2021

Preprint

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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 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 of the iris area of the 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 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.

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