Palmprint recognition using Kekre's wavelet's energy entropy based feature vector
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
A competitive sample selection method for palmprint recognition
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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This paper proposed an enhanced algorithm of palmprint recognition. The 2D Gabor was done firstly to filter in the main direction and strengthen the primary line’s information. Then we adopted wavelet transform to decompose the palmprint image, and extract the low frequency component. Two-Dimensional Principal Component Analysis(2DPCA) can avoid transforming from image matrix to 1D vector so as to reduce the computational complexity and gain the eigenvalue of image. However, some noises will affect the algorithm due to the tiny rotation and squeezing in the samples collection. In order to improve the traditional 2DPCA, and increase the recognition rate of palmprints, the paper applied the Moment Invariance. It is not sensitive to the noise mentioned above, and can prevent from being influenced by them. This paper combined the two methods, and calculated the eigenvalue again and again, then matched each other by nearest distance rule. The experiment shows that 2DPCA combining with moment invariances can improve recognition rate compare to 2DPCA.