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Proceedings Paper

Multiresolution ARMA modeling of facial color images
Author(s): Mehmet Celenk; Inad Al-Jarrah
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Paper Abstract

Human face perception is the key to identify confirmation in security systems, video teleconference, picture telephony, and web navigation. Modeling of human faces and facial expressions for different persons can be dealt with by building a point distribution model (PDM) based on spatial (shape) information or a gray-level model (GLM) based on spectral (intensity) information. To avoid short-comings of the local modeling of PDM and GLM, we propose a new approach for recognizing human faces and discriminating expressions associated with them in color images. It is based on the Laplacian of Gaussian (LoG) edge detection, KL transformation, and auto-regressive moving average (ARMA) filtering. First, the KL transform is applied to the R, G, and B dimensions, and a facial image is described by its principal component. A LoG edge-detector is then used for line drawing schematic of a face. The resultant face silhouette is divided into 5 X 5 non-overlapping blocks, each of which is represented by the auto-regressive (AR) parameter vector a. The ensample average of a over the whole image is taken as the feature vector for the description of a facial pattern. Each face class is represented by such ensample average vector a. Efficacy of the ARMA model is evaluated by the non-metric similarity measure S equals a.b/a.b for two facial images whose feature vectors, and a and b, are the ensample average of their ARMA parameters. Our measurements show that the ARMA modeling is effective for discriminating facial features in color images, and has the potential of distinguishing the corresponding facial expressions.

Paper Details

Date Published: 22 May 2002
PDF: 8 pages
Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002);
Show Author Affiliations
Mehmet Celenk, Ohio Univ. (United States)
Inad Al-Jarrah, Ohio Univ. (United States)

Published in SPIE Proceedings Vol. 4667:
Image Processing: Algorithms and Systems
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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