基于图像分解的人脸特征表示

摘要

This paper presents a face feature representation method based on image decomposition (FRID). FRID first decomposes an image into a series of orientation sub-images by executing multiple orientations operator. Then, each orientation sub-image is decomposed into a real part image and an imaginary part image by applying Euler mapping operator. For each real and imaginary part image, FRID divides them into multiple non-overlapping local blocks. The real and imaginary part histograms are calculated by accumulating the number of different values of image blocks respectively. All the real and imaginary part histograms of an image are concatenated into a super-vector. Finally, the dimensionality of the super-vector is reduced by linear discriminant analysis to yield a low-dimensional, compact, and discriminative representation. Experimental results show that FRID achieves better results in comparison with state-of-the-art methods, and is the most stable method.%提出一种基于图像分解的人脸特征表示方法(FRID)，首先通过多方向操作，把一幅图像分解成一系列方向子图像；然后，通过欧拉映射操作，把每幅方向子图像分解成实部和虚部图像，针对每幅实部和虚部图像，分别划分出多个不重叠的局部图像块，通过统计图像块上不同数值的个数生成相应的实部和虚部直方图，一幅图像的所有实部和虚部直方图被串联成一个超级特征向量；最后，利用线性判别分析方法对超级特征向量进行维数约简，以获得每幅图像的低维表示。实验显示该方法在多个人脸数据库上获得了优于时新算法的识别结果，并且表现得更为稳定。