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Proceedings Paper

Face classification using curvature-based multiscale morphology
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Paper Abstract

In this paper, we present a novel technique for classification of face images that employs Curvature-based Multi-scale Morphology (CMM). Multi-scale Morphology is an image analysis technique that employs mathematical morphology with structuring elements whose spatial dimensions are scaled successively. This scale-space representation of images has proven to be an efficient technique for indexing a large database of images. A majority of the existing techniques for multi-scale morphology employ regular and symmetrical structuring elements like cylinders, hemispheres or circular poweroids. The shape of these structuring elements is controlled only by the scaling parameter. In this paper, we propose the use of a structuring element whose shape is a function of both the scaling factor and the principal curvatures of the intensity surface of the face image. A high-dimensional feature vector is obtained by applying the CMM technique to the face images. The dimensionality of the feature vector is reduced by using the PCA technique, and the low- dimensional feature vectors are analyzed using an Enhanced FLD Model (EFM) for superior classification performance. Experimental results have shown that the proposed CMM technique outperforms existing approaches based on multi- scale morphology.

Paper Details

Date Published: 4 January 2002
PDF: 12 pages
Proc. SPIE 4671, Visual Communications and Image Processing 2002, (4 January 2002); doi: 10.1117/12.453095
Show Author Affiliations
Madhusudhana Gargesha, Arizona State Univ. (United States)
Sethuraman Panchanathan, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 4671:
Visual Communications and Image Processing 2002
C.-C. Jay Kuo, Editor(s)

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