An Universal Automatic Signature Verification with Explainability

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K11942
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 61010:Perceptual information processing-related
• Research Institution: Tokyo Denki University (2022-2023); Saitama Institute of Technology (2021)
• Principal Investigator: Ohyama Wataru 東京電機大学, システム デザイン 工学部, 教授 (10324550)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: バイオメトリクス / 署名照合 / 機械学習

Outline of Final Research Achievements

The automatic signature verification using machine learning technology still faces challenges in (1) improving the explainability of the verification decision basis, (2) enhancing verification accuracy, and (3) increasing the universality to uniformly verify signatures in various languages. This study primarily addresses (1) the development of technology that can explain the basis of verification decisions in automated signature verification using machine learning, (2) the advancement of automated signature verification by utilizing methods such as combinatorial partitioned signature verification, ranking learning, and handwriting verification focusing on local variations, and (3) the realization of a "universal signature verification" that can uniformly verify signatures in various languages.

Free Research Field

情報工学

Academic Significance and Societal Importance of the Research Achievements

署名照合は国際的には社会的に広く受け入れられている本人確認手法である．本研究の成果は，署名照合の自動化に残されていた上述の課題を解決する糸口になることが期待される．