Hand geometry identification without feature extraction by general regression neural network
Expert Systems with Applications: An International Journal
Ensemble of multiple Palmprint representation
Expert Systems with Applications: An International Journal
A survey of palmprint recognition
Pattern Recognition
Bimodal personal recognition using hand images
Proceedings of the International Conference on Advances in Computing, Communication and Control
Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Personal authentication using multiple palmprint representation
Pattern Recognition
A user dependent multi-resolution approach for biometric data
International Journal of Information Technology and Management
Biometric recognition using feature selection and combination
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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This paper investigates the performance of a bimodal biometric system using fusion of shape and texture. We propose several new hand-shape features that can be used to represent the hand shape and improve the performance for hand-shape based user authentication. We also demonstrate the usefulness of Discrete Cosine Transform (DCT) coefficients for palmprint authentication. The score level fusion of hand shape and palmprint features using product rule achieves best performance as compared to Max or Sum rule. The two hand shapes of an individual are anatomically similar. However, the palmprint information from the two hands can be combined to further improve performance and is investigated in this paper. Our experimental results on the database of 100 users achieve promising results and therefore confirm the usefulness of proposed method.