Statistical analysis with missing data
Statistical analysis with missing data
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification by Directional Image Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fingerprint Classification by Directional Fields
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Signals, Systems, and Transforms
Signals, Systems, and Transforms
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Fingerprint classification based on decision tree from singular points and orientation field
Expert Systems with Applications: An International Journal
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The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% is obtained on the five-class problem. This performance evaluation is done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002).