Capturing micro deformations from pooling layers for offline signature verification

Yuchen Zheng, Wataru Ohyama, Brian Kenji Iwana, Seiichi Uchida

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

4 被引用数 (Scopus)

抄録

In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the max-pooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writer-dependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS-150, GPDS-300, GPDS-1000, GPDS-2000, and GPDS-5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve state-of-the-art results on these datasets.

本文言語英語
ホスト出版物のタイトルProceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
出版社IEEE Computer Society
ページ1111-1116
ページ数6
ISBN(電子版)9781728128610
DOI
出版ステータス出版済み - 9 2019
イベント15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 - Sydney, オーストラリア
継続期間: 9 20 20199 25 2019

出版物シリーズ

名前Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN(印刷版)1520-5363

会議

会議15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
国/地域オーストラリア
CitySydney
Period9/20/199/25/19

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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