Laplace平滑变换及其在人脸识别中的应用

Laplacian smoothing transform for face recognition

本文主要研究如何从最优化的角度出发,从图像中提取低频特征.首先,基于图像的局部梯度定义了一种图像频率,并基于这种定义,诱导出Laplace平滑变换(LST),将二维图像映射到一维的向量.然后,将LST与学习算法相结合,提出二步子空间学习算法.所提的基于LST的二步子空间方法,对于光照、表情、姿势具有鲁棒性.实验表明,在ORL,Yale和FERET人脸数据库上,基于LST的人脸识别算法,相对DCT,DWT和PCA等预处理算法,具有更小的识别误差.

In this paper,we investigate how to extract the lowest frequency features from an image.A novel laplacian smoothing transform（LST） is proposed to transform an image into a sequence,by which low frequency features of an image can be easily extracted for a discriminant learning method for face recognition.Generally,the LST is able to be a efficient dimensionality reduction method for face recognition problems.Extensive experimental results show that the LST method performs better than other pre-processing methods,such as discrete cosine transform（DCT）,principal component analysis（PCA） and discrete wavelet transform（DWT）,on ORL,Yale and PIE face databases.Under the leave one out strategy,the best performance on the ORL and Yale face databases is 99.75% and 99.4%,however,in this paper,we improve both to 100% with a fast linear feature extraction method for the first time.