Integrating shape and texture for hand verification
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 confirms the usefulness of the proposed method.