Clustering-based discriminative locality alignment for face gender recognition

Publication Type:
Conference Proceeding
IEEE International Conference on Intelligent Robots and Systems, 2012, pp. 4156 - 4161
Issue Date:
Filename Description Size
Thumbnail2012004228OK.pdf531.31 kB
Adobe PDF
Full metadata record
To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality alignment (CDLA) algorithm to discover the low-dimensional intrinsic submanifold from the embedding high-dimensional ambient space for improving the face gender recognition performance. In particular, CDLA exploits the global geometry through k-means clustering, extracts the discriminative information through margin maximization and explores the local geometry through intra cluster sample concentration. These three properties uniquely characterize CDLA for face gender recognition. The experimental results obtained from the FERET data sets suggest the superiority of the proposed method in terms of recognition speed and accuracy by comparing with several representative methods. © 2012 IEEE.
Please use this identifier to cite or link to this item: