关键词: 特征提取,局部鉴别高斯模型,人脸识别 中图法分类号TP391.41文献标识码A

Abstract: Feature extraction plays an important role in face recognition．In general,feature extraction methods need to transfer the 2D images into 1D vectors．As a result,it is hard to calculate the covariant matrix and eigen-vector efficiently and exactly due to the high dimensionality．This paper proposed a new feature extraction method named 2D local discriminant Gaussian (2D-LDG)．It inherits the properties of LDG and the objective function of proposed method is also an approximation to the leave-one-out training error of a local quadratic discriminant analysis classifier．Also,it applies local Gaussians to eastimate probability in each point,relaxing the assumption on the class probability density function．Meanwhile,2D-LDG is operated on 2D images directly which avoids turning the image matrixes into high dimensional vectors,and is able to calculate the covariance matrix and eigen-vector in a more efficient and accurate way．Experiments on ORL and YaleB-Extended show that our proposed 2D-LDG feature extraction method achieves better performance in face recognition.

Key words: Feature extraction,Local discriminative Gaussians model,Face recognition