A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Biometric verification/identification based on hands natural layout
Image and Vision Computing
Biometric identification by means of hand geometry and a neural net classifier
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Two unconstrained biometric databases
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Two unconstrained biometric databases
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
| Hi-index | 0.00 |
Hand-geometry biometric recognition is normally based on the detection of five points that correspond to the fingertips and four points between them (valley points). Specific methods often have to be implemented during the acquisition stage to make the detection of those points easier. This study presents techniques that have been developed to overcome the difficulties and limitations of the current systems. Moreover, a hand-geometry based recognition system that has no constraints during image acquisition is presented. A methodology was developed based on the hand skeleton for the points on the fingertips and for the valley points it was based on the curvature of the hand contour. The principal difficulties were found during the segmentation step, which often fails if the fingers are not spread out. Once the points have been located, the necessary features for authentication were extracted. Classification algorithms were implemented at this stage. Those showing the best results presented a Genuine Acceptance Rate (GAR) of 76% and 8% for the False Acceptance Rate (FAR).