A multi-matcher for ear authentication
Pattern Recognition Letters
A survey of palmprint recognition
Pattern Recognition
Robust Biometric System Using Palmprint for Personal Verification
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Personal identification using periocular skin texture
Proceedings of the 2010 ACM Symposium on Applied Computing
A novel representation of palm-print for recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Stockwell transform based palm-print recognition
Applied Soft Computing
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Theories and applications of LBP: a survey
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Palmprint based recognition system using local structure tensor and force field transformation
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Palmprint based recognition system using phase-difference information
Future Generation Computer Systems
Biometric feature extraction with biometric specific shape descriptors
International Journal of Biometrics
Selection of discriminative sub-regions for palmprint recognition
Multimedia Tools and Applications
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Local Binary Pattern (LBP) is a powerful texture descriptor that is gray-scale and rotation invariant [3]. Because texture is one of the most clearly observable features in low-resolution palmprint images, we think local binary pattern based features are very discriminative for palmprint identification. In this paper, we propose a palmprint identification approach using boosted local binary pattern based classifiers. The palmprint area is scanned with a scalable subwindow from which local binary pattern histograms [4] are extracted to represent the local features of a palmprin image. The multi-class problem is transformed into a two-class one of intra- and extraclass by classifying every pair of palmprint images as intra-class or extra-class ones[19]. We use the AdaBoost[18] algorithm to select those sub-windows that are more discriminative for classification. Weak classifiers are constructed based on the Chi square distance between two corresponding local binary pattern histograms. Experiments on the UST-HK palmprint database show competitive performance.