Abstract

This paper presents an approach to face alignment under variable illumination, an obstacle largely ignored in previous 2D alignment work. To account for illumination variation, our method employs two forms of relatively lighting-invariant information. Edge phase congruency is adopted to coarsely localize facial features, since prominent feature edges can be robustly located in the presence of shading and shadows. To accurately deal with features with less pronounced edges, final alignment is then computed from intrinsic gray-level information recovered using a proposed form of local intensity normalization. With this approach, our face alignment system works efficiently and effectively under a wide range of illumination conditions, as evidenced by extensive experimentation.

Keywords:
Artificial intelligence Computer vision Normalization (sociology) Computer science Invariant (physics) Facial recognition system Face (sociological concept) Pattern recognition (psychology) Mathematics

Metrics

28
Cited By
2.31
FWCI (Field Weighted Citation Impact)
17
Refs
0.89
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Face recognition and analysis
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Generative Adversarial Networks and Image Synthesis
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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