In this thesis, a novel method for fingerprint recognition using wavelet transform and Principal Component Analysis (PCA) developed using MATLAB is presented. Fingerprint images used for this purpose are taken in grey-scale without any pre processing (i.e., smoothing, minutiae extraction, ridge thinning and ridge segmentation). Fingerprint images are chosen in such a way that the core point is located at the center of the image. In order to reduce the computational time and for efficient memory usage, the grey-scale image is decomposed using wavelets. The approximate coefficients of the resultant decomposed image serves as the fingerprint database. The proposed algorithm is tested on a database using PCA and recognition rate of more than 90% is obtained. This algorithm can be used on a desktop computer to recognize small group of users effectively and efficiently.In this thesis, a novel method for fingerprint recognition using wavelet transform and Principal Component Analysis (PCA) developed using MATLAB is presented.
|Title||:||Fingerprint Recognition Using Wavelets and Principal Component Analysis|
|Author||:||Avinash Hnhalli Ramalingegowda|
|Publisher||:||ProQuest - 2006|