A Basic Study on Multimodal Biometric Person Authentication : Signature, Face and Voice

多模态生物识别个人认证的基础研究：签名、面部和语音

• 批准号: 13650431
• 负责人: MATSUMOTO Takashi
• 金额: $ 2.24万
• 依托单位: Waseda University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13650431/
• 关键词: Signature verification; Face verification; Biometric person authentication; Multimodal person authentication; バイオメトリックス; マルチモーダル; 顔照合; 声紋; HMM; MCMC; ペントラジェクトリー; バイオメトリック認識; 顔特徴

The algorithms used to verify personal identity can roughly be classified into the following four categories : static, dynamic, biometric, and physical/knowledge-based. For instance, fingerprints, iris, retina, DNA, face and blood vessels are static and biometric. Algorithms classified as biometric and dynamic include lip movements, body movements and on-line signatures. Schemes that use passwords are static and knowledge-based, whereas methods using IC cards, magnetic cards, and key are physical.This research project investigated the following biometric schemes as the first step toward multimodal authentication :1. online signatureWe have proposed several different algorithms. The most recent one uses Bayesian method with MCMC (Markov Chain Monte Carlo). FRR of 0.93% and FAR of 0.73% were simultaneously achieved.2. faceAfter segmenting a face subimage from a natural image data, nine feature points were esitiamted using Gabor wevelette features together with MCMC. A great improvements over the previous sheme has been observed.2. voiceSeveral algorithms have been tried.Overall fusion of the three methods are still in its infancy and will be a topic of future project.

用于验证个人身份的算法大致可分为以下四类：静态、动态、生物识别和基于物理/知识的算法。例如，指纹、虹膜、视网膜、DNA、面部和血管都是静态的和生物识别的。生物识别和动态算法包括嘴唇运动、身体运动和在线签名。使用密码的方案是静态的和基于知识的，而使用IC卡、磁卡和钥匙的方法是物理的。本研究项目研究了以下生物识别方案，作为实现多模态认证的第一步：我们提出了几种不同的算法。最近的一个使用贝叶斯方法与MCMC（马尔可夫链蒙特卡罗）。FRR为0.93%，FAR为0.73%。从自然图像数据中分割出人脸子图像后，结合Gabor小波特征和MCMC估计出9个特征点。与以前的方案相比，已经观察到很大的改进。已经尝试了几种算法。三种方法的整体融合仍处于起步阶段，将是未来项目的主题。

项目成果

On-line signature verification algorithm incorporating pen position, pen pressure and pen inclination trajectories

• DOI: 10.1109/icassp.2001.941084
• 发表时间: 2001-05
• 期刊: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
• 作者: D. Sakamoto;H. Morita;T. Ohishi;Y. Komiya;T. Matsumoto

An Approach of Grouping Decomposed Components

一种分解组件分组的方法

• 发表时间: 2003
• 期刊: 4th International Symposium on Independent Component Analysis and Blind Signal Separation
• 作者: Daisuke Ito;Takuya Mukai;Noboru Murata
• 通讯作者: Noboru Murata

On line Signature Verifier Incorporating Position, Pressure, Inclination and Velocity Trajectories

结合位置、压力、倾斜度和速度轨迹的在线签名验证器

• 发表时间: 2002
• 期刊: The 2002 International Conference on Security and Management (CD-ROM)
• 作者: M.Kondo;T.Matsumoto;et al.
• 通讯作者: et al.

A New Online Signature Verification Algorithm Incorporating Pen Velocity Trajectories

一种结合笔速度轨迹的新在线签名验证算法

• 发表时间: 2002
• 期刊: Int'l Symposium on Intelligent Signal Processing and Communication Systems (CD-ROM)
• 作者: Mitsuru Kondo;Takashi Matsumoto;et al.
• 通讯作者: et al.

ペン位置・筆圧・傾き・時間情報を用いたオンライン書名照合

使用笔位置、压力、倾斜和时间信息进行在线书名匹配

• 发表时间: 2001
• 期刊: 情報処理学会第63回全国大会講演論文集 3
• 作者: 坂本大輔;松本隆;他
• 通讯作者: 他