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RI | Publications | Face Refinement through a Gradient Descent Alignment Approach
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Face Refinement through a Gradient Descent Alignment Approach
S. Lucey and I. Matthews
HCSNet Workshop on the Use of Vision in HCI (VisHCI), November, 2006.
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| Abstract |
The accurate alignment of faces is essential to almost all automatic tasks involving face analysis. A common paradigm employed for this task is to exhaustively evaluate a face template/classifier across a discrete set of alignments (typically translation and scale). This strategy, provided the template/classifier has been trained appropriately, can give one a reliable but ``rough'' estimate of where the face is actually located. However, this estimate is often too poor to be of use in most face analysis applications (e.g. face recognition, audio-visual speech recognition, expression recognition, etc.). In this paper we present an approach that is able to refine this initial rough alignment using a gradient descent approach, so as to gain adequate alignment. Specifically, we propose an efficient algorithm which we refer to as the sequential algorithm, which is able to obtain a good balance between alignment accuracy and computational efficiency. Experiments are conducted on frontal and non-frontal faces.
| Text Reference |
S. Lucey and I. Matthews, "Face Refinement through a Gradient Descent Alignment Approach," HCSNet Workshop on the Use of Vision in HCI (VisHCI), November, 2006.
| BibTeX Reference |
@inproceedings{Lucey_2006_5564,
author = "Simon Lucey and Iain Matthews",
title = "Face Refinement through a Gradient Descent Alignment Approach",
booktitle = "HCSNet Workshop on the Use of Vision in HCI (VisHCI)",
month = "November",
year = "2006"
}