Abstract
Visual perception of faces is invariant under many transformations, perhaps the most problematic of which is pose change (face rotating in depth). We use a variation of Gabor wavelet transform (GWT) as a representation framework for investigating face pose measurement. Dimensionality reduction using principal components analysis (PCA) enables pose changes to be visualised as manifolds in low-dimensional subspaces and provides a useful mechanism for investigating these changes. The effectiveness of measuring face pose with GWT representations was examined using PCA. We discuss our experimental results and draw a few preliminary conclusions.
Original language | English |
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Pages | 265-270 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Dec 1996 |
Event | Proceedings of the 1996 2nd International Conference on Automatic Face and Gesture Recognition - Killington, VT, USA Duration: 14 Oct 1996 → 16 Oct 1996 |
Conference
Conference | Proceedings of the 1996 2nd International Conference on Automatic Face and Gesture Recognition |
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City | Killington, VT, USA |
Period | 14/10/96 → 16/10/96 |
Keywords
- Principal component analysis
- Face detection
- Face recognition
- Robustness
- Wavelet transforms
- Solid modeling
- Eyes
- Lighting
- Machine vision
- Laboratories
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition