A simple and effective technique for human verification with Hand Geometry

This paper presents a new hand geometry based human verification technique which is efficient, simple, fast, easy to handle and cost effective compared to other verification techniques. Hand geometry is a popular biometric type in verification process. This hand geometry based verification comprises of two main attributes, (1) feature extraction by image processing and (2) feature learning by artificial neural network (ANN). For feature learning, distance based nearest neighbor (DBNN) algorithm has been applied. Using this approach, experimental results show 99.11% total success rate (TSR), 2.97% false acceptance rate (FAR) and 0% false rejection rate (FRR) (using 250 samples). A comparison is made between proposed method and published alternative hand geometry based methods at the end of this paper. The experimental results demonstrate that the proposed method outperforms the existing methods.