mlsanityData to 7 Oct 2026

A Unified Framework for Iris Anti-Spoofing: Introducing Iris Anti-Spoofing Cross-Domain-Testing Protocol and Masked-MoE Method

arXiv 2408.09752 ↗First posted 19 Aug 2024cs.CVcited by 0

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Abstract (arXiv)

Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, iris images captured by different devices exhibit certain and device-related consistent differences, which has a greater impact on the classification algorithm for anti-spoofing. The iris of various races would also affect the classification, causing the risk of identity theft. So it is necessary to improve the cross-domain capabilities of the iris anti-spoofing (IAS) methods to enable it more robust in facing different races and devices. However, there is no existing protocol that is comprehensively available. To address this gap, we propose an Iris Anti-Spoofing Cross-Domain-Testing (IAS-CDT) Protocol, which involves 10 datasets, belonging to 7 databases, published by 4 institutions, and collected with 6 different devices. It contains three sub-protocols hierarchically, aimed at evaluating average performance, cross-racial generalization, and cross-device generalization of IAS models. Moreover, to address the cross-device generalization challenge brought by the IAS-CDT Protocol, we employ multiple model parameter sets to learn from the multiple sub-datasets. Specifically, we utilize the Mixture of Experts (MoE) to fit complex data distributions using multiple sub-neural networks. To further enhance the generalization capabilities, we propose a novel method Masked-MoE (MMoE), which randomly masks a portion of tokens for some experts and requires their outputs to be similar to the unmasked experts, which can effectively mitigate the overfitting issue of MoE. For the evaluation, we selected ResNet50, VIT-B/16, CLIP, and FLIP as representative models and benchmarked them under the proposed IAS-CDT Protocol.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 71 references that are in the map. First posted on arXiv 19 Aug 2024.

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30 of 71 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
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  9. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  10. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 834
  11. 2021Scaling Vision with Sparse Mixture of Expertscited by 740
  12. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 609
18 more
  1. 2022ST-MoE: Designing Stable and Transferable Sparse Expert Modelscited by 391
  2. 2023From Sparse to Soft Mixtures of Expertscited by 245
  3. 2024A Survey on Mixture of Experts in Large Language Modelscited by 233
  4. 2022Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Expertscited by 202
  5. 2022A Review of Sparse Expert Models in Deep Learningcited by 147
  6. 2023Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformerscited by 59
  7. 2023FLIP: Cross-domain Face Anti-spoofing with Language Guidancecited by 45
  8. 2024CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofingcited by 39
  9. 2021CYBORG: Blending Human Saliency Into the Loss Improves Deep Learningcited by 33
  10. 2021Sparse MoEs meet Efficient Ensemblescited by 19
  11. 2024Unified Physical-Digital Face Attack Detectioncited by 17
  12. 2020Learning Target Domain Specific Classifier for Partial Domain Adaptationcited by 16
  13. 2020CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detectioncited by 16
  14. 2021Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Networkcited by 15
  15. 2022MoEC: Mixture of Expert Clusterscited by 14
  16. 2022Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translationcited by 8
  17. 2020Deep convolutional neural networks for face and iris presentation attack detection: Survey and case studycited by 6
  18. 2024Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysiscited by 5

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  4. 2024BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
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