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. However the decisions from the Sum, Max, and Product rules can also be combined to further enhance the performance. Thus the fusion of score level decisions, from the multiple strategies, is proposed and investigated. The two hand shapes of an individual are anatomically similar. However, the palmprints from two hands can be combined to further improve performance and is demonstrated in this paper.