Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Utilizing Finger Surface Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-matcher system based on knuckle-based features
Neural Computing and Applications
Person recognition using facial video information: A state of the art
Journal of Visual Languages and Computing
Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
Journal of Visual Languages and Computing
Personal authentication using finger knuckle surface
IEEE Transactions on Information Forensics and Security
Online finger-knuckle-print verification for personal authentication
Pattern Recognition
An innovative contactless palm print and knuckle print recognition system
Pattern Recognition Letters
Ensemble of local and global information for finger-knuckle-print recognition
Pattern Recognition
A Two-Stage Cross Correlation Approach to Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal identification using knuckleprint
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Phase congruency induced local features for finger-knuckle-print recognition
Pattern Recognition
Palm line extraction and matching for personal authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
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Objective: This paper proposed a new approach for inner-knuckle-print (IKP) recognition. In traditional IKP recognition systems, the region of interest (ROI) is extracted from the image of the whole hand and the directions of the fingers being imaged are not restricted. The result maybe incorrect because that the shape and surface of the fingers may vary greatly. Moreover, if the direction of the finger being imaged is not restricted, there may be severe rotation transform between intra-class IKPs. To overcome these drawbacks, we develop a new data acquisition scheme as well as an efficient personal authentication algorithm. Methods: The new scheme is designed to capture the image of the inner surface of the middle knuckles of the middle and ring fingers. The fingers being imaged are kept horizontal with two pegs, so that the rotation angle between different images obtained from the same hand can be minimized. The new personal authentication algorithm consists of the next four steps. Firstly, two regions of interest (ROI), each of which contains the inner surface of a knuckle, are cropped from the original image. Secondly, line features are extracted from the ROIs based on the combination of Gabor filtering and derivative line detection method. Then, binary line images are matched by using a cross-correlation-based method. Finally, the input data is classified through score level fusion. Results: To evaluate the proposed IKP recognition system, a finger image database which includes 2000 images from 100 volunteers is established. The images are captured on two separate occasions, at an interval of around two months. Most of the volunteers are not familiar with the image acquisition process. The experimental results show that the proposed system achieves high recognition rate and it works in real time. Moreover, the proposed line feature extraction method outperforms traditional Gabor filter based line detection method and derivative line detection method in accuracy. Conclusion: The proposed IKP system is robust and accurate. It may promote the application and popularization of IKP recognition.