Online signature verification approach using Mellin transform and empirical mode decomposition

Hafs, Toufik; Bennacer, Layachi; Naït‐Ali, Amine; Boughazi, Mohamed; Bouzid-Daho, Abdellatif

doi:10.1109/biosmart.2017.8095311

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Online authentication can obtain more dynamic structure information, so that the authentication accuracy is higher, which has been carried out for a long time. Calvozaragoza J et al [2] introduced Hidden Markov Model to handle the task of handwritten note recognition; Hafs T et al [3] applied Mellin Transform and Empirical Mode Decomposition to English handwritten signature authentication and achieved good results. Online signature authentication usually requires special devices, which cannot meet the actual requirements and application scenarios in most cases.…”

Section: Introductionmentioning

confidence: 99%

Offline handwritten signature authentication based on perceptual hash and DFT

Chen

Qian

Liu

et al. 2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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In order to solve the problem of sparse and missing example in the off-line handwritten signature authentication scenario, a handwritten signature authentication tree algorithm was proposed. The algorithm improves the perceptual hash algorithm based on the discrete Fourier transform, which is used to gather the subject information in the signature image, compress and generate the perceptual digest; then the cosine distance is used to calculate the distance between the perceptual digest vectors. According to the cosine distance matrix, an authentication tree composed by multiple nodes is constructed and the distance threshold is determined to authenticate the unknown data. The authentication result can be used for the self-renewal of the authentication tree. The experimental results show that the handwritten signature authentication tree algorithm has high accuracy and strong robustness. The false rejection rate and false acceptance rate on small datasets are significantly lower than the traditional machine learning algorithm, and its self-renewal mechanism can also cope with the style changes of signature handwriting very well.

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Section: Introductionmentioning

confidence: 99%