<title>Hand recognition by wavelet transforms and neural networks</title>

Abstract

A new approach to human hand recognition is presented. It combines concepts from image segmentation, contour representation, wavelet transforms, and neural networks. With this approach, people are distinguished by their hands. After obtaining a person's hand contour, each finger of the hand is located and separated based on its points of sharp curvature. A two dimensional (2-D) finger contour is then mapped to a one dimensional (1-D) functional representation of the boundary called a finger signature. The wavelet transform then decomposes the finger signature signal into lower resolutions retaining the most significant features. The energy at each stage of the decomposition is calculated to extract the features of each finger. A three layer artificial neural network with back propagation training is employed to measure the performance of the wavelet transform. A database consisting of five hand images obtained from twenty-eight different people is used in the experiment. Three of the images are used for training the neural network. The other two are used for testing the algorithm. Results presented illustrate high accuracy human recognition using this scheme.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.