LBP has been widely applied in texture classification and face recognition as a kind of texture description operator for its simplicity and high efficiency. Given that the features that are extracted by the basic LBP and its variant-LDP operator are sensitive to noise

only the symbol information of the difference among local pixels is used for encoding. The binarization method is too simple to extract adequate texture feature information. Thus

this paper proposes a face recognition algorithm based on LDP through polarization encoding. First

the first-order derivatives along the 0°

45°

90°

and 135° directions are obtained. Second

the Stokes vector of the face image is built. Third

the texture feature of the face image is extracted from multiple directions. Fourth

following the encoding method of the azimuth of polarization

each sub-block histogram vector with varying weights is calculated according to the image entropy to constitute the final face feature vector via cascading. The experiments obtain correct recognition rates of 97.4% and 92.22% in the ORL and YALE face databases

respectively

the used time is almost the same with LBP and LDP algorithm. When the sample size is large

the complexity is lower than LBP method. In the presence of gaussian noise and salt and pepper noise

we respectively obtain correct recognition rates of 93.88%

86.27% and 96.13%

84.71%

they are much higher than LBP and LDP algorithm. The proposed algorithm based on polarization encoding can extract more discriminating texture features and achieve a high face recognition rate even in the presense of noise. This algorithm also has some reference values for texture classification and object recognition in other fields.