Fingerprint Classification with Combinations of Support Vector Machines

By Yuan Yao, Paolo Frasconi, Massimiliano Pontil. 2001

78 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety Research, Signal Processing

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Octavio Loyola-González
2. Adnan Amin
3. Neil Yager

In Safety Research

1. Octavio Loyola-González
2. Adnan Amin
3. Neil Yager

In Signal Processing

1. Octavio Loyola-González
2. Adnan Amin
3. Neil Yager

Citations in the corpus

• Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “One of the most outstanding application areas for the SVMs is biometrics [13], [14], [24]–[28].”
• Fingerprint classification: a review (2004, methodology): “The authors found that vector-based features (FingerCodes) had a much higher classification accuracy.”