The finger knuckle print (FKP) of a particular person is found to be unique and can serve as a biometric feature has been revealed recently by the researchers. In this paper finger knuckle print will be used as a biometric feature. The databaseImages from Hong KongPolytechnic Universitywere processed using Kekre's hybrid wavelet type 1 and type 2 for the generation of results. Kekre's hybrid wavelet type 1 and type 2 were used for feature extraction from the images in order to process it further. The important role of hybrid wavelet transform is to combine the key features of two different orthogonal transforms so that the strengths of both the transform wavelets are used. The hybrid wavelet transforms can be generated using orthogonal transforms such as Discrete Cosine transform (DCT), Walsh transform, Discrete Kekre transform etc. In this paper the different transforms like (Discrete Cosine Transform) DCT, Haar. Hartley, Walsh and Kekre are used in any combination for generation of hybrid wavelets. These hybrid wavelets are applied on the database images to generate feature vector coefficients plotted in graph format and their distances are compared. The intra class and inter class distances are compared in this paper.

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Finger knuckle print (FKP) hybrid wavelet region of interest (ROI) Transform.