期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2012
卷号：4
页码：374-382
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：This paper explores the use of a series of Graph Em- bedding (GE) algorithms based on Neighborhood Discriminant Embedding (NDE) as a means to improve the performance and robustness of face recognition. NDE combines GE framework and Fishers criterion and it takes into account the Individual Discriminative Factor (IDF) which is proposed to describe the microscopic discriminative property of each sample. The ten- sor and bilinear extending algorithms of NDE are proposed for directly utilizing the original two-dimensional image data to en- hance the efficiency. The common purpose of our algorithms are to gather the within-class samples closer and separate the between-class samples further in the projected feature subspace after the dimensionality reduction. Furthermore, another in- formative feature extraction method called Circular Pixel Dis- tribution (CPD) is applied to enhance the robustness of the 2-D algorithm. Experiments with the Olivetti Research Laboratory (ORL) face dataset are conducted to evaluate our methods in terms of classification accuracy, efficiency and robustness.
