Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Competitive Coding Scheme for Palmprint Verification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Palmprint Identification Using PalmCodes
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal authentication using finger knuckle surface
IEEE Transactions on Information Forensics and Security
Human identification using Knucklecodes
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Personal identification using knuckleprint
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Application of Projective Invariants in Hand Geometry Biometrics
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Image Processing
Personal recognition using hand shape and texture
IEEE Transactions on Image Processing
Identity verification by using handprint
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A contactless biometric system using multiple hand features
Journal of Visual Communication and Image Representation
A finger-knuckle-print recognition algorithm using phase-based local block matching
Information Sciences: an International Journal
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This paper presents a multimodal biometric identification system based on finger geometry, knuckle print and palm print features of the human hand. The hand image captured from digital camera was first preprocessed to get the finger ROI (Region Of Interest) and palm ROI. Finger geometry features and knuckle print features of index, middle, ring and little fingers were extracted from the finger ROI; palm print features represented with keypoints and their local descriptors were extracted from palm ROI. A coarse-to-fine hierarchical method was employed to match multiple features for efficient hand recognition in a large database. The decision level AND rule fusion was adopted which has shown the improvement of the combined scheme. Our experimental results demonstrate the feasibility and effectiveness of the proposed method.