Hand shape identification using neural networks

Karen O. Egiazarian; Santiago Gonzalez Pestana

doi:10.1117/12.468007

22 May 2002 Hand shape identification using neural networks

Karen O. Egiazarian, Santiago Gonzalez Pestana

Proceedings Volume 4667, Image Processing: Algorithms and Systems; (2002) https://doi.org/10.1117/12.468007
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

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.

Citation Download Citation

Karen O. Egiazarian and Santiago Gonzalez Pestana "Hand shape identification using neural networks", Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); https://doi.org/10.1117/12.468007

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