HMM-based on-line signature verification: Feature extraction and signature modeling

By Julian Fierrez, Javier Ortega-Garcia, Daniel Ramos, Joaquin Gonzalez-Rodriguez. Pattern Recognition Letters, 2007

345 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/19116f131348477e5b8f6c822de0255b551269e9

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. Almas Abbasi
3. Robert Sabourin
4. Woo Chaw Seng
5. Luiz Gustavo Hafemann
6. J. Unar

In Computer Vision and Pattern Recognition

1. Luiz Oliveira
2. Almas Abbasi
3. Robert Sabourin
4. Woo Chaw Seng
5. Luiz Gustavo Hafemann
6. J. Unar

In Information Systems

1. Luiz Oliveira
2. Almas Abbasi
3. Robert Sabourin
4. Woo Chaw Seng
5. Luiz Gustavo Hafemann
6. J. Unar

Citations in the corpus

• Learning features for offline handwritten signature verification using deep convolutional neural networks (2017, methodology): “Using the model trained with genuine signatures, we obtained EER of 3.91% using a linear SVM, and 3.13% using the RBF kernel.”
• A review of biometric technology along with trends and prospects (2014, methodology): “Table 1: Summary of hand region modalities: Modality Feature Set Recognition Techniques Datasets Fingerprint Ridge flow, ridge pattern, singular points, ridge skeleton, ridge flow, ridge ending, ridge contours, ridge kernel, orientation field, island, spur, crossover, learned feature, sweat pores, d”