Multilingual-signature verification by verifier fusion using random forests

Keg Matsuda, Wataru Oyama, Tetsushi Wakabayashi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

In this paper, we propose an improvement to the method of combined segmentation verification for multi-script signature verification. The proposed method employs a fusion strategy for multiple signature verifiers using different modalities, i.e., offline and online signature verification. We use offline signature shape features extracted from separated three color plane (RGB) images that reflect the pen pressure and pen velocity of the signature signers. The Mahalanobis distance for each offline feature vector is calculated for signature verification. In addition, we employ another offline feature based on the grayscale histogram and similarity between histograms for offline signature verification. The online feature-based technique employs a dynamic programming matching technique for the time series data of the signatures. These matching results are fused using a verification classifier for making the final decision. Conventionally, a support vector machine (SVM) has been used for the verification classifier. We investigate the performance and feasibility of a random forest (RF) for the verification classifier instead. The results of evaluation experiments using the SigComp multi-script signature dataset show that the proposed method improves the performance and that RF outperforms SVM.

本文言語英語
ホスト出版物のタイトルProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ911-916
ページ数6
ISBN(電子版)9781538633540
DOI
出版ステータス出版済み - 12 13 2018
外部発表はい
イベント4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, 中国
継続期間: 11 26 201711 29 2017

出版物シリーズ

名前Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017

その他

その他4th Asian Conference on Pattern Recognition, ACPR 2017
Country中国
CityNanjing
Period11/26/1711/29/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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