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Auflistung nach Autor:in "Abe, Narishige"

1 - 3 von 3
  • Konferenzbeitrag
    A biometric key-binding scheme using lattice masking
    (BIOSIG 2014, 2014) Sugimura, Yuka; Yasuda, Masaya; Yamada, Shigefumi; Abe, Narishige; Shinzaki, Takashi
    Template protection technology can protect the confidentiality of a biometric template by certain conversion. We focus on the key-binding approach for template protection. This approach generates a secure template (or a conversion template) from joint data of a user's specific key with a user's template, and the key can be correctly extracted from the secure template only when a queried biometric feature is sufficiently close to the original template. While almost all conventional schemes use the error correcting code (ECC) technique, we present a new technique based on lattices to give a new key-binding scheme. Our proposed scheme can provide several requirements (e.g., diversity and revocability) for template protection, which cannot be provided by ECC-based schemes such as the fuzzy commitment and the fuzzy vault.
  • Konferenzbeitrag
    Learning by Environment Cluster s for Face Presentation Attack Detection
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Matsunami, Tomoaki; Uchida, Hidetsugu; Abe, Narishige; Yamada, Shigefumi
    Face recognition has been used widely for personal authentication. However, there is a problem that it is vulnerable to a presentation attack in which a counterfeit such as a photo is presented to a camera to impersonate another person. Although various presentation attack detection methods have been proposed, these methods have not been able to sufficiently cope with the diversity of the heterogeneous environments including presentation attack instruments (PAIs) and lighting conditions. In this paper, we propose Learning by Environment Clusters (LEC) which divides training data into some clusters of similar photographic environments and trains bona-fide and attack classification models for each cluster. Experimental results using Replay-Attack, OULU-NPU, and CelebA-Spoof show the EER of the conventional method which trains one classification model from all data was 20.0%, but LEC can achieve 13.8% EER when using binarized statistical image features (BSIFs) and support vector machine used as the classification method
  • Konferenzbeitrag
    A novel local feature for eye movement authentication
    (Biosig 2016, 2016) Abe, Narishige; Yamada, Shigefumi; Shinzaki, Takashi