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An Adaptive Face-Iris Multimodal Identification System Based on Quality Assessment Network

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MultiMedia Modeling (MMM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12572))

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Abstract

In practical applications, face-iris multimodal recognition systems may suffer from performance degradation when image acquisition is not constrained strictly. One way to diminish the negative effect of poor-quality samples is to incorporate quality assessment (QA) into face-iris fusion schemes. However, existing face and iris QA approaches are limited by specific types of distortions or requiring particular reference images. To tackle this problem, an adaptive face-iris multimodal identification system based on quality assessment network is proposed. In the system, the face-iris quality assessment network (FaceIrisQANet) can measure face-iris relative quality scores given their image features, achieving distortion-generic and referenceless QA. Different from most deep neural networks, the FaceIrisQANet employs biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit (ReLU) as activation function. Additionally, face and iris are assigned adaptive weights according to their relative quality scores at the score level fusion scheme. Experimental results on three face-iris multimodal datasets show that our system not only provides a good recognition performance but also exhibits superior generalization capability.

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Notes

  1. 1.

    http://biometrics.idealtest.org.

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Acknowledgment

This work was supported in part by NSFC-Shenzhen Robot Jointed Founding under Grant U1613215, in part by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing), and in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010137001.

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Authors and Affiliations

  1. School of Electronic and Computer Engineering, Peking University, Beijing, China

    Zhengding Luo, Qinghua Gu, Guoxiong Su, Yuesheng Zhu & Zhiqiang Bai

Authors
  1. Zhengding Luo
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  2. Qinghua Gu
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  3. Guoxiong Su
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  4. Yuesheng Zhu
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  5. Zhiqiang Bai
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Corresponding author

Correspondence to Yuesheng Zhu .

Editor information

Editors and Affiliations

  1. Charles University, Prague, Czech Republic

    Jakub Lokoč

  2. Charles University, Prague, Czech Republic

    Tomáš Skopal

  3. Klagenfurt University, Klagenfurt, Austria

    Klaus Schoeffmann

  4. CERTH-ITI, Thessaloniki, Greece

    Vasileios Mezaris

  5. Renmin University of China, Beijing, China

    Xirong Li

  6. CERTH-ITI, Thessaloniki, Greece

    Stefanos Vrochidis

  7. Queen Mary University of London, London, UK

    Ioannis Patras

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Luo, Z., Gu, Q., Su, G., Zhu, Y., Bai, Z. (2021). An Adaptive Face-Iris Multimodal Identification System Based on Quality Assessment Network. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12572. Springer, Cham. https://doi.org/10.1007/978-3-030-67832-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-67832-6_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-67832-6

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