Palmprint identification using wavelet energy

Abstract

Palmprint identification is the means of recognizing an individual from the database using his/ her palmprint features. Palmprint is easy to capture, requires cheaper equipment and is more acceptable by the public. Moreover, palmprint is also rich in features. Wavelet transform is a multi-resolution analysis tool that can extract palm lines in different resolution levels. In low-resolution level, fine palm lines are extracted. The higher the resolution level, the coarser are the extracted palm lines. In this work, a digital camera is used to acquire the ten right hand image of 100 different individuals. The hand images are pre-processed to find the key points. By referring to the key point, the palmprint images are rotated and cropped. The palmprint images are enhanced and resized. The resized images are decomposed using different types of wavelets for six decomposition levels. Two different wavelet energy representations are tested. The feature vectors are compared to the database using Euclidean Distance or classified using feedforward backpropagation neural network. From the results, an accuracy of 99.07 percent can be obtained using Db5 wavelet energy feature type 2 and classified with neural network.