Personal Authentication Using Hand Geometry

This paper presents an approach to personal verification and identification using hand geometry. Having extracted, fifteen features of users´ right hand, Fingers´ width, Area and circumference, are classified with two different pattern recognition systems Euclidean and Absolute Distance. The algorithm has been tested on 500 pictures of 50 users. Result shows 99.7056 identification rate and 0.3198% Equal error rate (EER) with absolute distance classifier. As the pictures are captured with a scanner without any special instrument for fixing the placement, the purposed method is an inexpensive and easy to use. The experimental result shows the system performance in identification and verification.