Hand shape identification using neural networks
Karen Egiazarian, Santiago G. Pestana · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
A biometric identification system based on the user's hand-palm is presented. Two main approaches for feature extraction are explored: (a) geometrical (a set of geometrical measurements i.e. fingers' length, hand's area and perimeter are obtained from the user's hand), (b) by using the hand-palm contour with no further information. The large amount of data obtained by using the second approach leads us to a dimensionality reduction problem. We address this problems using three different solutions, contour down-sampling, PCA (Principal Component Analysis) and Wavelet decomposition. Two well known classification techniques, KNN (K-Nearest Neighbor) and NN (Neural Networks) are used to identify the users. Experimental results comparing each of these techniques are given.