Offline handwritten signature authentication based on perceptual hash and DFT

Musheng Chen; Youxiao Qian; Quanliang Liu; Junhua Wu

doi:10.1117/12.2672192

28 April 2023 Offline handwritten signature authentication based on perceptual hash and DFT

Musheng Chen, Youxiao Qian, Quanliang Liu, Junhua Wu

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126104N (2023) https://doi.org/10.1117/12.2672192
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

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.

Citation Download Citation

Musheng Chen, Youxiao Qian, Quanliang Liu, and Junhua Wu "Offline handwritten signature authentication based on perceptual hash and DFT", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126104N (28 April 2023); https://doi.org/10.1117/12.2672192

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KEYWORDS

Fourier transforms

Deep learning

Education and training

Matrices

Tunable filters

Image compression

Image enhancement

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