Reliability Function and Strong Converse of Biometrical Identification Systems Based on List-Decoding
Summary :
The biometrical identification system, introduced by Willems et al., is a system to identify individuals based on their measurable physical characteristics. Willems et al. characterized the identification capacity of a discrete memoryless biometrical identification system from information theoretic perspectives. Recently, Mori et al. have extended this scenario to list-decoding whose list size is an exponential function of the data length. However, as the data length increases, how the maximum identification error probability (IEP) behaves for a given rate has not yet been characterized for list-decoding. In this letter, we investigate the reliability function of the system under fixed-size list-decoding, which is the optimal exponential behavior of the maximum IEP. We then use Arimoto's argument to analyze a lower bound on the maximum IEP with list-decoding when the rate exceeds the capacity, which leads to the strong converse theorem. All results are derived under the condition that an unknown individual need not be uniformly distributed and the identification process is done without the knowledge of the prior distribution.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.5 pp.1262-1266
- Publication Date
- 2017/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.1262
- Type of Manuscript
- LETTER
- Category
- Information Theory
Authors
Vamoua YACHONGKA
The University of Electro-Communications
Hideki YAGI
The University of Electro-Communications
Keyword
Latest Issue
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Vamoua YACHONGKA, Hideki YAGI, "Reliability Function and Strong Converse of Biometrical Identification Systems Based on List-Decoding" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 5, pp. 1262-1266, May 2017, doi: 10.1587/transfun.E100.A.1262.
Abstract: The biometrical identification system, introduced by Willems et al., is a system to identify individuals based on their measurable physical characteristics. Willems et al. characterized the identification capacity of a discrete memoryless biometrical identification system from information theoretic perspectives. Recently, Mori et al. have extended this scenario to list-decoding whose list size is an exponential function of the data length. However, as the data length increases, how the maximum identification error probability (IEP) behaves for a given rate has not yet been characterized for list-decoding. In this letter, we investigate the reliability function of the system under fixed-size list-decoding, which is the optimal exponential behavior of the maximum IEP. We then use Arimoto's argument to analyze a lower bound on the maximum IEP with list-decoding when the rate exceeds the capacity, which leads to the strong converse theorem. All results are derived under the condition that an unknown individual need not be uniformly distributed and the identification process is done without the knowledge of the prior distribution.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1262/_p
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@ARTICLE{e100-a_5_1262,
author={Vamoua YACHONGKA, Hideki YAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Reliability Function and Strong Converse of Biometrical Identification Systems Based on List-Decoding},
year={2017},
volume={E100-A},
number={5},
pages={1262-1266},
abstract={The biometrical identification system, introduced by Willems et al., is a system to identify individuals based on their measurable physical characteristics. Willems et al. characterized the identification capacity of a discrete memoryless biometrical identification system from information theoretic perspectives. Recently, Mori et al. have extended this scenario to list-decoding whose list size is an exponential function of the data length. However, as the data length increases, how the maximum identification error probability (IEP) behaves for a given rate has not yet been characterized for list-decoding. In this letter, we investigate the reliability function of the system under fixed-size list-decoding, which is the optimal exponential behavior of the maximum IEP. We then use Arimoto's argument to analyze a lower bound on the maximum IEP with list-decoding when the rate exceeds the capacity, which leads to the strong converse theorem. All results are derived under the condition that an unknown individual need not be uniformly distributed and the identification process is done without the knowledge of the prior distribution.},
keywords={},
doi={10.1587/transfun.E100.A.1262},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Reliability Function and Strong Converse of Biometrical Identification Systems Based on List-Decoding
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1262
EP - 1266
AU - Vamoua YACHONGKA
AU - Hideki YAGI
PY - 2017
DO - 10.1587/transfun.E100.A.1262
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2017
AB - The biometrical identification system, introduced by Willems et al., is a system to identify individuals based on their measurable physical characteristics. Willems et al. characterized the identification capacity of a discrete memoryless biometrical identification system from information theoretic perspectives. Recently, Mori et al. have extended this scenario to list-decoding whose list size is an exponential function of the data length. However, as the data length increases, how the maximum identification error probability (IEP) behaves for a given rate has not yet been characterized for list-decoding. In this letter, we investigate the reliability function of the system under fixed-size list-decoding, which is the optimal exponential behavior of the maximum IEP. We then use Arimoto's argument to analyze a lower bound on the maximum IEP with list-decoding when the rate exceeds the capacity, which leads to the strong converse theorem. All results are derived under the condition that an unknown individual need not be uniformly distributed and the identification process is done without the knowledge of the prior distribution.
ER -
English
