Study of a committee of neural networks for biometric hand-geometry recognition
This is an incremental improvement for biometric security systems, focusing on hand-geometry recognition.
The paper tackled the problem of improving biometric hand-geometry recognition by comparing committees of neural networks against a multi-start initialization algorithm, finding that committees can enhance recognition rates, though no strong correlation exists between identification and verification tasks using the same classifier.
This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared with a multi-start initialization algo-rithm that just picks up the neural net which offers the best performance. On the other hand, we found that there is no strong correlation between identifi-cation and verification applications using the same classifier.