Analysis of the Performance Improvement Obtained by a Genetic Algorithm-based Approach on a Hand Geometry Dataset

Biometric recognition by hand geometry has a large number of measurements that may be used for authentication. The higher number of attributes, the harder is to define the importance of each one. In this paper, we analyze the use of a Genetic Algorithm-based approach in improving Equal Error Rate (EER) performance for biometric authentication by hand geometry. We used an own data set of dorsal and palm images of hand in a controlled environment to validate our approach. As the best results, the genetic algorithm decreased the equal error rate up to 0% in the training set and 0.01% for the test set. Additionally, a relative improvement of 90.91% was achieved by GA in the best case for the test set.

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