Multilingual-signature verification by verifier fusion using random forests

Keg Matsuda, Wataru Ohyama, Tetsushi Wakabayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538633540
Publication statusPublished - Dec 13 2018
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: Nov 26 2017Nov 29 2017

Publication series

NameProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017


Other4th Asian Conference on Pattern Recognition, ACPR 2017

All Science Journal Classification (ASJC) codes

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


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