Fusion and recognition of face and iris feature based on wavelet feature and KFDA

Abstract

In this paper, a novel approach to the fusion and recognition of face and iris image based on wavelet features and Kernel Fisher Discriminant Analysis (KFDA) is developed. Firstly, the dimension is reduced, the noise is eliminated, the storage space is saved and the efficiency is improved by Discrete Wavelet Transform (DWT) to face and iris image. Secondly, face and iris features are extracted and fusion by KFDA. Finally, Nearest Neighbor classifier is selected to perform recognition. Experimental results on ORL face database and CASIA iris database show that not only the ‘small sample problem’ is overcome by KFDA, but also the correct recognition rate is higher than that of face recognition and iris recognition.