Recognition of facial images using support vector machines

信号处理 Pub Date : 2001-08-06 DOI:10.1109/SSP.2001.955324

Kwang In Kim, J. Kim, K. Jung

引用次数: 9

Abstract

A novel support vector machine (SVM)-based method for appearance-based face recognition is presented. The proposed method does not use any external feature extraction process. Accordingly the intensities of the raw pixels that make up the face pattern are fed directly to the SVM. However, it takes account of prior knowledge about facial structures in the form of a kernel embedded in the SVM architecture. The new kernel efficiently explores spatial relationships among potential eye, nose, and mouth objects and is compared with existing kernels. Experiments with the ORL database show a recognition rate of 98% and speed of 0.22 seconds per face with 40 classes.

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基于支持向量机的人脸图像识别

提出了一种基于支持向量机的基于外观的人脸识别方法。该方法不使用任何外部特征提取过程。因此，组成人脸图案的原始像素的强度直接被馈送到支持向量机。然而，它以嵌入在支持向量机架构中的核的形式考虑了面部结构的先验知识。新核可以有效地探索潜在的眼、鼻、口目标之间的空间关系，并与现有核进行比较。在ORL数据库上进行的实验表明，该方法的识别率为98%，每张脸的识别率为0.22秒。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.