文章基本信息

标题：Face Recognition and Gender Classification Using Orthogonal Nearest Neighbour Feature Line Embedding
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作者：Gang-Feng Ho ; Ying-Nong Chen ; Chin-Chuan Han 等
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2012
卷号：9
DOI：10.5772/51752
语种：English
出版社：SAGE Publications
摘要：In this paper, a novel manifold learning algorithm for face recognition and gender classification - orthogonal nearest neighbour feature line embedding (ONNFLE) - is proposed. Three of the drawbacks of the nearest feature space embedding (NFSE) method are solved: the extrapolation/interpolation error, high computational load and non-orthogonal eigenvector problems. The extrapolation error occurs if the distance from a specified point to one line is small when that line passes through two farther points. The scatter matrix generated by the invalid discriminant vectors does not efficiently preserve the locally topological structure - incorrect selection reduces recognition. To remedy this, the nearest neighbour (NN) selection strategy was used in the proposed method. In addition, the high computational load was reduced using a selection strategy. The last problem involved solving the non-orthogonal eigenvectors found with the NFSE algorithm. The proposed algorithm generated orthogonal bases possessing more discriminating power. Experiments were conducted to demonstrate the effectiveness of the proposed algorithm.
关键词：Nearest Feature Line; Face Recognition; Gender Classification; Extrapolation; Orthogonal Basis