| Technology of biometrics uses human body characteristics for automated personal authentication. Because biometric features are universal, unique, stable, unforgettable and hard to imitate and forge. In contrast to the traditional methods, biometrics is now becoming a new way to meet security and convenience demand for personal identification.Personal authentication based on palmprint is a new branch of this area in the coming years. Palmprints contain more distinctive information than fingerprints, have less instruments cost than iris, obtain higher accuracy than face recognition, are easier to be accepted by clients, and are also more stable than signatures. All these characteristics make palmprint recognition as a hotspot soon after its appearance in the last few years. However, limited work has been reported on palmprint identification and verification at present.The design of a palmprint recognition system involves image data collection, image preprocessing, feature extraction, feature matching. This paper concentrates on the deeply research of the image preprocessing and feature extraction in palmprint identification system. It proposes a series of efficient palmprint preprocessing, feature extraction, feature matching algorithms, which can reach a 98.8% recognition rate. On the basis of proper localization, principal components analysis (PCA) can depict the distribution of main palmprint features, and define the projection subspace of palmprint by calculating the eigenvectors of the covariance matrix of palmprint images. In order to overcoming the deficiency of lage scale of calculation, a new method for feature extraction of palmprint is proposed. This method combines wavelet decomposing with PCA, which improves upon feature extraction speed of palmprint under without reducing recognition rate. It gets a satisfied experimental result showing the improvement in recognition rate and computational complexity based on analysis method of wavelet decomposition and PCA. |
PDF Full Text Request