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Face Refinement through a Gradient Descent Alignment Approach

Workshop Paper, HCSNet '06 Workshop on the Use of Vision in HCI (VisHCI '06), Vol. 56, pp. 43 - 49, 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.

BibTeX

@workshop{Lucey-2006-9619,
author = {Simon Lucey and Iain Matthews},
title = {Face Refinement through a Gradient Descent Alignment Approach},
booktitle = {Proceedings of HCSNet '06 Workshop on the Use of Vision in HCI (VisHCI '06)},
year = {2006},
month = {November},
volume = {56},
pages = {43 - 49},
keywords = {Object detection, Gradient descent},
}
2021-04-19T13:16:06-04:00

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