A survey of palmprint recognition
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
A palmprint recognition algorithm using phase-based correspondence matching
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Online joint palmprint and palmvein verification
Expert Systems with Applications: An International Journal
Implementation of remote system using touchless palmprint recognition algorithm
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Robust Classification Method of Tumor Subtype by Using Correlation Filters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Newborn footprint recognition using band-limited phase-only correlation
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
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We propose a palmprint classification algorithm with the use of multiple correlation filters per class. Correlation filters are two-class classifiers that produce a sharp peak when filtering a sample of their class and a noisy output otherwise. For every class, we train the filters for a palm at different locations, where the palmprint region has a high degree of line content. With the use of a line detection procedure and a simple line energy measure, any region of the palm can be scored and the top-ranked regions are used to train the filters for each class. Using an enhanced palmprint segmentation algorithm, our proposed classifier achieves an average equal error rate of 1.12 times10-4% on a large database of 385 classes using multiple filters of size 64 times 64 pixels. The average false acceptance rate when the false rejection rate is zero is 2.25 times10-4%.