Human face recognition based on multidimensional PCA and extreme learning machine
Human face recognition based on multidimensional PCA and extreme learning machine
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DOI：
10.1016/j.patcog.2011.03.013
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发表时间：
2011-10-01
影响因子：
8
通讯作者：
Sid-Ahmed, M. A.
中科院分区：
文献类型：
--
作者：
Mohammed, A. A.;Minhas, R.;Sid-Ahmed, M. A.
提出了一种基于双向二维主成分分析（B2DPCA）和极限学习机（ELM）的人脸识别算法。该方法是基于曲波图像分解的人脸和一个子带，表现出最大的标准差是维数减少使用改进的降维技术。使用B2DPCA生成判别特征集，以确定分类精度。其他显着的贡献，拟议的工作包括分类率的显着改善，高达百倍减少训练时间和最小的依赖于原型的数量。使用具有挑战性的数据库进行了广泛的实验，并将结果与最先进的技术进行了比较。(C)2011爱思唯尔有限公司保留所有权利。
In this work, a new human face recognition algorithm based on bidirectional two dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) is introduced. The proposed method is based on curvelet image decomposition of human faces and a subband that exhibits a maximum standard deviation is dimensionally reduced using an improved dimensionality reduction technique. Discriminative feature sets are generated using B2DPCA to ascertain classification accuracy. Other notable contributions of the proposed work include significant improvements in classification rate, up to hundred folds reduction in training time and minimal dependence on the number of prototypes. Extensive experiments are performed using challenging databases and results are compared against state of the art techniques. (C) 2011 Elsevier Ltd. All rights reserved.