A new sparse image representation algorithm applied to facial expression recognition

信号处理 Pub Date : 2004-09-29 DOI:10.1109/MLSP.2004.1423017

I. Buciu, Ioannis Pitas

引用次数: 80

Abstract

In this paper, we present a novel algorithm for learning facial expressions in a supervised manner. This algorithm is derived from the local non-negative matrix factorization (LNMF) algorithm, which is an extension of non-negative matrix factorization (NMF) method. We call this newly proposed algorithm discriminant non-negative matrix factorization (DNMF). Given an image database, all these three algorithms decompose the database into basis images and their corresponding coefficients. This decomposition is computed differently for each method. The decomposition results are applied on facial images for the recognition of the six basic facial expressions. We found that our algorithm shows superior performance by achieving a higher recognition rate, when compared to NMF and LNMF

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一种新的稀疏图像表示算法应用于面部表情识别

在本文中，我们提出了一种新的基于监督的面部表情学习算法。该算法由局部非负矩阵分解(LNMF)算法衍生而来，是对非负矩阵分解(NMF)方法的扩展。我们将这种新提出的算法称为判别非负矩阵分解(DNMF)。给定一个图像数据库，这三种算法都将数据库分解为基图像及其对应的系数。对于每种方法，这种分解的计算方式是不同的。将分解结果应用于人脸图像，对六种基本面部表情进行识别。我们发现，与NMF和LNMF相比，我们的算法在实现更高的识别率方面表现出了卓越的性能

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.