2013
DOI: 10.1002/sec.829
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Score level fusion approach in dynamic signature verification based on hybrid wavelet-Fourier transform

Abstract: In this work, a dynamic handwritten signature verification system based on hybrid discrete wavelet transform and discrete Fourier transform is presented. Wavelet transform is adopted to analyze the signature information in a multi‐resolution representation while retaining its local information by decomposing the signature into different sub‐bands. Fourier transform is performed to extract the feature descriptor of the decomposed sub‐bands. A dissimilarity score of the extracted features between the test signat… Show more

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Cited by 13 publications
(3 citation statements)
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“…A review of research literature on integrated transform domain approaches for data merging (fusion) revealed that generally, these approaches are implemented in either hybrid models or multi-model [8]. The research contributions using data fusion models include the robust watermarking scheme [9] and signature verification [10]. Other closely related contributions in the research literature that unified both DFT and DWT include compressed sensing [11][12][13] and defect detection [14].…”
Section: Introductionmentioning
confidence: 99%
“…A review of research literature on integrated transform domain approaches for data merging (fusion) revealed that generally, these approaches are implemented in either hybrid models or multi-model [8]. The research contributions using data fusion models include the robust watermarking scheme [9] and signature verification [10]. Other closely related contributions in the research literature that unified both DFT and DWT include compressed sensing [11][12][13] and defect detection [14].…”
Section: Introductionmentioning
confidence: 99%
“…The forward feature selection algorithm was used to search for the best performing feature subsets. Khoh et al [3] presented a dynamic handwritten signature verification system based on hybrid DWT and DFT. Signature information in a multi-resolution representation was analyzed by DWT, and the feature descriptor of the decomposed sub-bands was extracted by DFT.…”
Section: Introductionmentioning
confidence: 99%
“…In order to select a quite good wavelet packet base, first assigns a sequence the cost function, then seeks in all wavelet packet base causes the cost function smallest base, assigned the vector regarding one, the minimum cost was the most effective expression, this base was called optimal base. Cost function and optimal base's selection method as follows [5]:…”
Section: D Face Feature Extraction Based On Wavelet Packetmentioning
confidence: 99%