Online Signature Verification Using Hybrid Wavelet Transform with Hidden Markov Model

Chavan, Manoj; Singh, Ravish R.; Bharadi, Vinayak Ashok

doi:10.1109/iccubea.2017.8463660

Cited by 4 publications

(2 citation statements)

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“…In [4] the authors proposed to verify the dynamic signature with the use of a hybrid wavelet transform and a hidden Markov model classifier. The hybrid wavelet transform is generated using the Kronecker product on the basis of two selected orthogonal transforms.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach

Zalasiński

Laskowski

Niksa-Rynkiewicz

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions.

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Section: Related Workmentioning

confidence: 99%

Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach

Zalasiński

Laskowski

Niksa-Rynkiewicz

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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“…The first 16 samples of the HWT output are used as feature vector. Discrete Cosine transform (DCT), Discrete Hartley transform (DHT), Discrete Walsh transform (DWT) and Discrete Kekre transform (DKT) are used to form the HWT[19,20]. There are many topologies of HMM such as Left to Right, Ergodic and Ring.…”

mentioning

confidence: 99%

Online signature verification using hybrid wavelet transform

Chavan¹,

Singh²,

Bharadi³

2020

IJECE

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Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vector is given to Left to Right HMM classifier to identify the genuine and forged signature. For HWT-1, DCT HAAR offers best FAR and FRR. . For HWT-2, KEKRE 128 offers best FAR and FRR. HWT-1 offers better performance than HWT- 2 in terms of FAR and FRR. As the number of states increase, the performance of the system improves. For HWT - 1, KEKRE 128 offers best performance at 275 symbols whereas for HWT - 2, best performance is at 475 symbols by KEKRE 128.

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