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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 905495, 15 pages
Research Article

Hand Recognition Using Thermal Image and Extension Neural Network

Department of Electrical Engineering, National Chin-Yi University of Technology, No. 35, Lane 215, Sec. 1, Chung-Shan Rd., Taichung County Taiping City, 411, Taiwan

Received 18 August 2011; Revised 7 October 2011; Accepted 12 October 2011

Academic Editor: Angelo Luongo

Copyright © 2012 Meng-Hui Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hand recognition is one of the popular biometry methods for access control systems. In this paper, a new scheme for personal recognition using thermal images of the hand and an extension neural network (ENN) is presented. The features of the recognition system are extracted from gray level hand images, which are taken by an infrared camera. The main advantage of the thermal image is that it can reduce errors and noise in the features extracted stage, which is most important to increase the accuracy of recognition systems. Moreover, a new recognition method based on the ENN is proposed to perform the core functions of the hand recognition system. The proposed ENN-based recognition method also permits rapid adaptive processing for a new pattern, as it only tunes the boundaries of classified features or adds a new neural node. It is feasible to implement the proposed method on a Microcomputer for a portable personal recognition device. From the tested examples, the proposed method has a significantly high degree of recognition accuracy and shows good tolerance to errors added.