Automatic Signature Verification: The State of the Art

By Donato Impedovo, Giuseppe Pirlo. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2008

759 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3f46b16dcaad1cf23806718bc51924ef91c4c5d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luiz Oliveira
2. Robert Sabourin
3. Luiz Gustavo Hafemann

In Computer Vision and Pattern Recognition

1. Luiz Oliveira
2. Robert Sabourin
3. Lin Zhong
4. Luiz Gustavo Hafemann

Citations in the corpus

• Learning features for offline handwritten signature verification using deep convolutional neural networks (2017, background): “Over the years, the problem has been addressed from many different perspectives, as summarized by [12], [13] and [2].”
• uWave: Accelerometer-based personalized gesture recognition and its applications (2009, background): “While many paradigms exist for user authentication, including password [19], biometrics [16–18], speech [20], and handwriting [21], accelerometer-based gesture recognition has its unique value for user authentication because of its low cost, high efficiency, and no change to the form factors of the ”