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Palmprint identification is the measurement of palmprint features for recognizing the identity of a user. Palmprint is universal, easy to capture and does not change much across time. Palmprint biometric system does not requires specialized acquisition devices. It is user-friendly and more acceptable by the public. Besides that, palmprint contains different types of features, such as geometry features, line features, point features, statistical features and texture features. In this work, peg-less right hand images for 100 different individuals were acquired ten times. No special lighting is used in this setup. The hand image is segmented and its key points are located. The hand image is aligned and cropped according to the key points. The palmprint image is enhanced and resized. Sequential modified Haar transform  is applied to the resized palmprint image to obtain modified haar energy (MHE) feature. The sequential modified Haar wavelet can maps the integer-valued signals onto integer-valued signals without abandoning the property of perfect reconstruction. The MHE feature is compared with the feature vectors stored in the database using Euclidean Distance. The accuracy of the MHE feature and Haar energy feature under different decomposition levels and combinations are compared. 94.3678 percent accuracy can be achieved using proposed MHE feature.