Contourlet Features Extraction and AdaBoost Classification for Palmprint Verification
Eisa Rezazadeh Ardabili, Keivan Maghooli, Emad Fatemizadeh · 2011
Biometrics-based personal verification is a powerful security features in technology era. Palmprint is an important complement and reliable biometric that can be used for identity verification because it is stable and unique for every individual. This paper presents a new palmprint verification method by using the contourlet features and AdaBoost classification. The contourlet transform is a new two dimensional extension of the wavelet transform using multi-scale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images. AdaBoost is used as a classifier in the experiments. Experimental results shows that the contourlet features when classify by using AdaBoost (α-Type) classifier are very suitable for invariant palmprint verification. The experimental results illustrate the effectiveness of the method proposed. (Eisa Rezazadeh Ardabili, Keivan Maghooli, Emad Fatemizadeh. Contourlet features extraction and AdaBoost classification for palmprint verification. Journal of American Science 2011;7(7):353-362). (ISSN: 1545-1003). http://www.americanscience.org.