Face Recognition Systems Using Relevance Weighted Two Dimensional Linear Discriminant Analysis Algorithm

• 作者: Hythem Ahmed Jedra Mohamed Zahid Noureddine
• 来源期刊: 信号与信息处理(英文)
• 学科: 工学
• 关键词: LDA PCA 2DLDA RW2DLDA Extraction FACE RECOGNITION Small SAMPLE Size
• 年，卷（期）: 2012,（1）
• 页码范围: 130-135
• 页数: 6页
• 分类号: TP39

摘要

Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.