On the Error-Reject Trade-Off in Biometric Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hand Recognition Using Implicit Polynomials and Geometric Features
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A single-sensor hand geometry and palmprint verification system
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Model-guided deformable hand shape recognition without positioning aids
Pattern Recognition
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A survey of biometric technology based on hand shape
Pattern Recognition
Automated flexion crease identification using internal image seams
Pattern Recognition
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Hand shape recognition based on coherent distance shape contexts
Pattern Recognition
Hand-geometry based recognition system: a non restricted acquisition approach
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Hand shape recognition using Hu and Legendre moments
Proceedings of the 6th International Conference on Security of Information and Networks
Genetic eigenhand selection for handshape classification based on compact hand extraction
Engineering Applications of Artificial Intelligence
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In this paper, a hand biometric system for verification and recognition purposes is presented. The method is based on three keys. Firstly, the system is based on using a Natural Reference System (NRS) defined on the hand's natural layout. Consequently, neither hand-pose training nor a pre-fixed position is required in the registration process. Secondly, the hand's features are obtained through the polar representation of the hand's contour. This implies minimum image processing and low computational cost. Thirdly, instead of common methods that use one hand, we use right and left hands. This allows us to consider distance measures for direct (R/R,L/L) and crossed (R/L,L/R) hands obtaining improvements in FAR/FRR and identification ratios. The paper shows details about the experimentation and presents the results of the method applied on 5640 images belonging to 470 users. The results are good enough to consider this biometric system for future security/control applications.