Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Computers in Biology and Medicine
WND-CHARM: Multi-purpose image classification using compound image transforms
Pattern Recognition Letters
Biometric identification using knee X-rays
International Journal of Biometrics
Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art
ACM Transactions on Applied Perception (TAP)
Biometrics beyond the visible spectrum: imaging technologies and applications
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Progression analysis and stage discovery in continuous physiological processes using image computing
EURASIP Journal on Bioinformatics and Systems Biology
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
Fake finger detection by skin distortion analysis
IEEE Transactions on Information Forensics and Security
How to Generate Spoofed Irises From an Iris Code Template
IEEE Transactions on Information Forensics and Security
| Hi-index | 0.00 |
While external body parts such as the face, fingerprints, or retina are often used for biometric identification, it can also be reasonably assumed that internal organs imaged with biomedical imaging devices can also allow biometric identification. Here we studied the use of MRI images for the purpose of biometric identification, and show that the accuracy of person identification using knee MRIs is significantly higher than random. Knee MRI images of 2,686 different patients were used in the experiment, and analysed using the wndchrm image classification scheme. Experimental results show that the rank-10 identification accuracy using the MRI knee images is ∼93% for a dataset of 100 individuals, and ∼45% for the entire dataset of 2,686 persons. Since MRI is used for the purpose of imaging internal parts of the body, this approach of biometric identification can potentially offer high resistance to deception.