The new hand geometry system and automatic identification

Abstract

For the past decades an extensive amount of time and effort has been consumed for the research and development of biometric-based recognition systems. One such system is the one that can recognize based on hand geometry. The objective of this thesis is to explore the usage of hand geometry for developing a hand geometry recognition system. This paper proposes a system performing automatic recognition without the use of specific hardware. The system emphases on executing feature extractions from a typical database and then developing a neural network classifier based on back-propagation architectures with various exercise methods.  Features are dug out by the use of morphological (segmentation) operation. Our Experiments were carried out on 500 images (50 persons, 10 images each) under distinctive conditions with possible deliberation of scaling, Translation, Rotation, Color and Illumination modification. The accurate recognition rate is about 96.41 % for the matching of artificial neural network which is calculated by the formula average of sum of errors divided over the number of images.