人脸识别中基于学习的核图像微分滤波器

Learning based kernel image differential filter for face recognition

针对人脸识别应用,提出一种基于学习且具有鉴别能力的核图像微分滤波器。首先,区别于现有滤波器的手工设计方法,该滤波器利用训练集动态学习获得,通过在学习过程中融入线性判别分析(LDA)思想,可在增加滤波后图像类内相似度的同时减小类间相似度;其次,在线性滤波分类器的基础上进一步引入二阶微分信息,并结合核方法在高维空间下进行滤波器学习,使得图像中的细节和非线性信息可以得到更好的利用并获得更具鉴别力的特征描述。AR和ORL人脸库上的多组对比实验结果表明,与线性可学习图像滤波器IFL、不考虑微分信息的核图像滤波器以及只考虑一阶微分信息的核图像滤波器进行比较,所提算法可有效提高识别性能。

For the applications of face recognition, a learning based kernel image differential filter was proposed. Firstly,instead of designing the image filter in a handcrafted or analytical way, the new image filter was designed by dynamically learning from the training data. By integrating the idea of Linear Discriminant Analysis（LDA） into filter learning, the intraclass difference of filtered image was attenuated and the inter-class difference was amplified. Secondly, the second order derivative operator and kernel trick were introduced to better extract the image detail information and cope with the nonlinear feature space problem. As a result, the filter is adaptive and more discriminative feature description can be obtained. The proposed algorithm was experimented on AR and ORL face database and compared with linearly learning image filter named IFL, kernel image filter without differential information, and kernel image filter considering only one order differential information. The experimental results validate the effectiveness of the proposed method.