Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities

@article{Bibi2019BiometricSA,
  title={Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities},
  author={Kiran Bibi and Saeeda Naz and Arshia Rehman},
  journal={Multimedia Tools and Applications},
  year={2019},
  volume={79},
  pages={289-340}
}
Biometric systems are playing a key role in the multitude of applications and placed at the center of debate in the scientific research community. Among the numerous biometric systems, handwritten signature verification has got keen interest over the last three decades. Handwritten signature verification is the behavioral bio-metric system that discriminates the genuine signature from the pre-stored known signatures. It has been researched in the number of application areas like banking… 
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17 Citations
Background Citations
3
Methods Citations
2

17 Citations

A Comprehensive Study on Offline Signature Verification
TLDR
This paper presents a comprehensive study of the work done in the field of offline signature verification and also the challenges which are still present in this area.
Offline Signature Biometric Verification with Length Normalization using Convolution Neural Network
TLDR
The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving, and outperforms the state-of-the-art works that are using the same SIGMA database.
BAMCloud: a cloud based Mobile biometric authentication framework
TLDR
A Cloud based mobile biometric authentication framework (BAMCloud) is proposed that uses dynamic signatures and performs authentication in a distributed manner over Cloud that achieves a speedup of 8.5x and a performance of 96.23%.
Online signature verification using signature down-sampling and signer-dependent sampling frequency
TLDR
The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing and proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency.
In-air signature verification system using Leap Motion
Using Residual Networks and Cosine Distance-Based K-NN Algorithm to Recognize On-Line Signatures
TLDR
An optimized transfer-learning-based architecture is proposed as a highly accurate identification technique for online-signatures using ResNet18 as a feature extraction deep-learning module and the robustness of the technique was proven by testing the generated models trained with one dataset with the other two datasets resulting in accuracy above 99%.
CNN-Based Classifier as an Offline Trigger for the CREDO Experiment
TLDR
To tag the artefacts appearing in the CREDO database with a CNN-based trigger, the method based on machine learning allows eliminating the manual supervision of the competition process.
A deep learning based review on abdominal images
TLDR
The state of the art of abdominal images for classifying abdominal organs based on deep learning is presented and is a useful for computer-aided diagnosis applications.
An adaptive genetic algorithm-based background elimination model for English text
TLDR
It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others.
A systematic review on machine learning models for online learning and examination systems
TLDR
A systematic review of the role of Machine learning in Lockdown Exam Management Systems was conducted by evaluating 135 studies over the last five years and concluded with issues and challenges that machine learning imposes on the examination system.
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References

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