Abstract：Aiming at what the existing face recognition systems identifying blurred face image cannot achieve desired results,a blurred face recognition method is proposed based on local phase quantization( LPQ) and Fisherfaces. Firstly,the LPQ histogram sequence( LPQHS) is extracted from block grey-level face images by the LPQ operator. Then the Fisherfaces based feature selection method is applied to extract feature subspace.Finally,the recognition is performed using a nearest neighbor principle. The algorithm enhances the effectiveness of extracting the blurred face texture information,so that the amount of training data is greatly reduced,and the dimension of feature vector of the image and the original image are independent of the size of a blurred face. The simulation experiments illustrate that the proposed method obtains a better recognition rate on both AR and YALE face database comparing to classical methods.

Keyword：local phase quantization;Fisherfaces;face recognition;recognition rate;LPQHS;