Face Refinement through a Gradient Descent Alignment Approach

Simon Lucey and Iain Matthews
HCSNet Workshop on the Use of Vision in HCI (VisHCI), December, 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.

Keywords
Object detection, Gradient descent

Notes

Text Reference
Simon Lucey and Iain Matthews, "Face Refinement through a Gradient Descent Alignment Approach," HCSNet Workshop on the Use of Vision in HCI (VisHCI), December, 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 = "December",
   year = "2006",
}