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Face Refinement through a Gradient Descent Alignment Approach

Simon Lucey and Iain Matthews
Workshop Paper, HCSNet Workshop on the Use of Vision in HCI (VisHCI), November, 2006

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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.

author = {Simon Lucey and Iain Matthews},
title = {Face Refinement through a Gradient Descent Alignment Approach},
booktitle = {Proceedings of HCSNet Workshop on the Use of Vision in HCI (VisHCI)},
year = {2006},
month = {November},
keywords = {Object detection, Gradient descent},
} 2019-06-27T17:06:42-04:00