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Palmprint based personal identification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a palmprint based identification approach which uses the textural information available on the palmprint by employing a feature level fusion of contourlet transform (CT) and non-subsampled contourlet transform (NSCT). The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. After establishing the region of interest (ROI), the two-dimensional (2-D) spectrum is divided into fine slices using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed separately for the two transforms. The features from both domains are then fused at feature levels. Palmprint matching is then performed using normalized …