Biometric verification of humans by means of hand geometry
This work addresses biometric identification for security applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackled biometric verification using hand geometry, achieving a maximum identification rate of 93.64% and a minimum detection cost function of 2.92% with a multi-layer perceptron classifier.
This paper describes a hand geometry biometric identification system. We have acquired a database of 22 people, 10 acquisitions per person, using a conventional document scanner. We propose a feature extraction and classifier. The experimental results reveal a maximum identification rate equal to 93.64%, and a minimum value of the detection cost function equal to 2.92% using a multi layer perceptron classifier.