Surface shape and curvature scales
Image and Vision Computing
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
COSMOS-a representation scheme for free-form surfaces
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Exploiting finger surface as a biometric identifier
Exploiting finger surface as a biometric identifier
Multi-biometrics using facial appearance, shape and temperature
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A fast algorithm for ICP-based 3D shape biometrics
Computer Vision and Image Understanding
Hand-based verification and identification using palm-finger segmentation and fusion
Computer Vision and Image Understanding
A survey of biometric technology based on hand shape
Pattern Recognition
Robust palmprint verification using 2D and 3D features
Pattern Recognition
Personal authentication using finger knuckle surface
IEEE Transactions on Information Forensics and Security
Palmprint recognition using 3-D information
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers' surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.