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YOLO NFPEM: A More Accurate Iris Detector

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Artificial Intelligence in HCI (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14051))

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Abstract

Iris detection remains vital application value in computer vision. Although the considerable advances and successes have been achieved by utilizing deep convolutional neural networks for iris detection, directly locating a small proportion of the iris from the full facial images still confronts considerable challenges. In this study, we proposed the YOLO NFPEM network, which employing the Feature Pyramid Enhancement Module (FPEM) cascaded to enhance and merge the different scale features (52 × 52, 26 × 26, 13 × 13) from the PEP7 layer (52 × 52), PEP15 layer (26 × 26) and PEP17 layer (13 × 13) of YOLO Nano network. YOLO NFPEM was train and tested on our presented multi-scale eye dataset (MSED) which contains full and partial facial images, and left/ right eye images. The results shown that YOLO NFPEM with three PEP modules cascaded achieves the best AP for iris of ~ 91.37% higher than YOLO Nano (~83.99%), YOLO Nano with enhanced FPN cascaded and the other YOLO NFPEM architectures, while still reaching a mAP of ~ 84.62%. Furthermore, we found an irreconcilable contradiction, considering the memory consumption and computational cost, neither the enhanced FPN nor FPEM module cascaded can achieve the best performance on both mAP and AP of iris. Testing results also shown that the small-size feature extraction and fusion capabilities of PEP modules cascaded are more powerful than FPN and enhanced FPN.

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Authors and Affiliations

  1. Beijing Institute of Computer Application, Beijing, PR China

    Xiangyu Ge, Tianqing Zhou, Fang Zhang, Zongrui Yang & Bingyuan Fan

  2. Shanghai United Imaging Healthcare Co., Ltd., Shanghai, PR China

    Chao Yin

  3. School of Biological Science and Medical Engineering, Beihang University, Beijing, PR China

    Qianxiang Zhou

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  1. Xiangyu Ge
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  2. Chao Yin
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  4. Tianqing Zhou
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  5. Fang Zhang
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  7. Bingyuan Fan
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Correspondence to Xiangyu Ge .

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Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Ge, X. et al. (2023). YOLO NFPEM: A More Accurate Iris Detector. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14051. Springer, Cham. https://doi.org/10.1007/978-3-031-35894-4_34

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  • DOI: https://doi.org/10.1007/978-3-031-35894-4_34

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