A novel palmprint authetication system by XWT based feature extraction and BFOA based feature selection and optimization

This paper is about a new palm-print based biometric authentication system. It is low-cost as it employs a scanner coupled to a PC, for acquiring the image of the human palm. The histogram of the palm-image is cross wavelet transformed(XWT) with respect to that of a reference palm-image. Several features are extracted from the resulting spectrum, and a trained ANN is used to identify the subject, based on these features. Bacterial Foraging Optimization Algorithm(BFOA) is used to select the combination of features that results in the best performance. An Adaptive Bacterial Foraging Optimization Algorithm (ABFOA) is also introduced to further augment the performance of the system. Extensive experimental investigations reveal that an authentication accuracy of more than 97% can be obtained with the system.