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 Euclidean distance classifier. The algorithm is tested on complete database of 7752 palm images of Polytechnic University of Hong Kong, and 500 palm images of GPDS Hand database from University of Las Palmas de Gran Canaria. The experimental results were compiled for features based upon individual transforms and fused one. CT based approach demonstrated the decidability index of 2.7734 and equal error rate (EER) of 0.2333% while NSCT based approach depicted decidability index of 2.8125 and EER of 0.1604% on palm database of Polytechnic University of Hong Kong. Similarly, CT based approach demonstrated the decidability index of 2.6212 and equal error rate (EER) of 0.7082% while NSCT based approach depicted decidability index of 2.7278 and EER of 0.5082% on GPDS hand database. The multimodal approach based upon feature fusion achieved decidability index of 2.8914 and EER of 0.1563% on database of Polytechnic University of Hong Kong and decidability index of 2.7956 and EER of 0.3112% on GPDS hand database. The quantitative measures confirm progressive improved results in three approaches for both the databases.