Biometric based personal identification is regarded as an effective method for automatically recognizing an individual's identity. As a method for preserving the security of sensitive information biometrics has been applied in various fields over last few decades. In our work, we present a novel core based global matching approach for fingerprint matching using the Contourlet Transform. The core and delta points along with the ridge and valley orientations have strong directionality or directional information. This directionality has been exploited as the features and considered for matching. The obtained ROI is analyzed for its textures using Contourlet transform which divides the 2-D spectrum into fine slices by employing Directional Filter Banks (DFBs). Distinct features are then extracted at different resolutions by calculating directional energies for each sub-block from the decomposed subband outputs, and given to a Euclidian distance classifier. Finally adaptive majority vote algorithm is employed in order to further narrow down the matching criterion. The algorithm has been tested on a developed database of 126 individuals, enrolled with 8 templates each.