Reducing the template ageing effect in on‐line signature biometrics

Tolosana, Rubén; Vera-Rodríguez, Rubén; Fiérrez, Julián; Ortega‐Garcia, Javier

doi:10.1049/iet-bmt.2018.5259

Cited by 28 publications

(12 citation statements)

References 38 publications

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“…First, we would like to highlight the impossibility of performing a fair comparison among approaches as different databases and experimental protocol conditions have been considered in each study. Aspects such as the inter-session variability, the number of training signatures available per user or the complexity of the signatures have a very significant impact in the system performance [28], [29]. This problem is not only related to deep learning approaches, but to the whole handwritten signature verification field.…”

Section: On-line Signature Verificationmentioning

confidence: 99%

DeepSign: Deep On-Line Signature Verification

Tolosana,

Vera-Rodriguez,

Fierrez

et al. 2020

Preprint

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Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered.The main contributions of this study are: i) we provide an indepth analysis of state-of-the-art deep learning approaches for on-line signature verification, ii) we present and describe the new DeepSignDB on-line handwritten signature biometric public database 1 , iii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and iv) we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) 2 for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user.

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Section: On-line Signature Verificationmentioning

confidence: 99%

DeepSign: Deep On-Line Signature Verification

Tolosana,

Vera-Rodriguez,

Fierrez

et al. 2020

Preprint

Self Cite

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“…It is also important to note that in the field of forensics, what separates an untrained person from a skilled investigator is the rate of false positives and the number of cases for which only an inconclusive opinion about the authors' identity may be expressed [9]. Many systems help to analyze a handwritten text [10][11][12][13][14], which utilizes such experts' features. There are also methods, based on signature analysis, that help diagnose a variety of illnesses, such as Parkinson's and Alzheimer's disease [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks

et al. 2021

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Handwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated analysis method for the dynamic electronic representation of handwritten signature authentication was researched. The developed algorithms are based on the dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the designed electronic pen described in the paper. The triplet loss method was used to train a neural network suitable for writer-invariant signature verification. For each signature, the same neural network calculates a fixed-length latent space representation. The hand-corrected dataset containing 10,622 signatures was used in order to train and evaluate the proposed neural network. After learning, the network was tested and evaluated based on a comparison with the results found in the literature. The use of the triplet loss algorithm to teach the neural network to generate embeddings has proven to give good results in aggregating similar signatures and separating them from signatures representing different people.

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“…On-line handwritten signature verification has always been a very active area of research due to its high popularity for authentication scenarios [9] and the variety of open challenges that are still under research nowadays [14], e.g., one/few-shot learning [20,10,27,45], device interoperability [2,30,36,44], aging [22,41], types of impostors [40,21], signature complexity [24,43,47], template storage [8], etc. Despite all these challenges, the performance of on-line signature verification systems has been improved in the last years due to several factors, especially: i) the evolution in the acquisition technology going from devices specifically designed to acquire handwriting and signature in office-like scenarios through a pen stylus (e.g.…”

Section: Introductionmentioning

confidence: 99%