• Title/Summary/Keyword: Writer-Dependent Features

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Online Signature Verification using Extreme Points and Writer-dependent Features (변곡점과 필자고유특징을 이용한 온라인 서명 인증)

  • Son, Ki-Hyoung;Park, Jae-Hyun;Cha, Eui-Young
    • Journal of Korea Multimedia Society
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    • v.10 no.9
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    • pp.1220-1228
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    • 2007
  • This paper presents a new system for online signature verification, approaching for finding gaps between a point-to-point matching and a segment-to-segment matching. Each matching algorithm has been separately used in previous studies. Various features with respect to each matching algorithm have been extracted for solving two-class classification problem. We combined advantages of the two algorithms to implement an efficient system for online signature verification. In the proposed method, extreme feints are used to extract writer-dependent features. In addition, using the writer-dependent features proves to be more adaptive than using writer-independent features in terms of efficiency of classification and verification in this paper.

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Writer verification using feature selection based on genetic algorithm: A case study on handwritten Bangla dataset

  • Jaya Paul;Kalpita Dutta;Anasua Sarkar;Kaushik Roy;Nibaran Das
    • ETRI Journal
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    • v.46 no.4
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    • pp.648-659
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    • 2024
  • Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.