Fitting of semantic wire-frames using principal components analysis of a set of facial images

A new, fast and efficient method of automatic fitting of wire-frames for semantic model-based coding of head-and-shoulders video sequences is proposed. The method utilises the principal components analysis (PCA) of a codebook of facial images. The PCA of the facial codebook is performed only once wh...

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Bibliographic Details
Published in:6th International Conference on Image Processing and its Applications pp. 351 - 355
Main Authors: Antoszczyszyn, P.M, Hannah, J.M, Grant, P.M
Format: Conference Proceeding
Language:English
Published: London IEE 1997
Subjects:
Applied sciences
Codes
Computer vision and image processing techniques
Exact sciences and technology
Image processing
Information, signal and communications theory
Optical information, image and video signal processing
Pattern recognition
Signal processing
Telecommunications and information theory
online processing
human face geometry
data compression
semantic wire-frames
algorithm speed
head and shoulders video sequences
video coding
automatic wire-frames fitting
image analysis
algorithm reliability
principal components analysis
facial images
semantic model based coding
facial codebook
face recognition
image sequences
Image processing
Principal component analysis
ISBN:085296692X, 9780852966921
ISSN:0537-9989
Online Access:Get full text
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Summary:A new, fast and efficient method of automatic fitting of wire-frames for semantic model-based coding of head-and-shoulders video sequences is proposed. The method utilises the principal components analysis (PCA) of a codebook of facial images. The PCA of the facial codebook is performed only once when all the images from the facial codebook are manually pre-fitted with semantic wire-frames of the same structure. Neither PCA nor manual wire-frame fitting of the codebook are a part of the on-line processing and so do not influence the speed of analysis of an unknown image. The algorithm consists of two stages. In stage one the approximate position of the subject's head is estimated. In stage two, the accurate positions of the important facial features (the left eye, the right eye, the lips and the nose) are established. Both stages use the codebook of facial images. The information about the geometry of the human face is utilised in the second stage only. This increases the speed and reliability of the algorithm. The results obtained after analysis of a widely used set of images are presented.
ISBN:085296692X
9780852966921
ISSN:0537-9989
DOI:10.1049/cp:19970914