Facial features detection robust to pose, illumination and identity

Abstract : This paper addresses the problem of automatic detection of salient facial features. Face images are described using local normalized gaussian receptive fields. Face features are learned using a clustering of the Gaussian derivative responses. We have found that a single cluster provides a robust detector for salient facial features robust to pose, illumination and identity. In this paper we describe how this cluster is learned and which facial features have found to be salient.
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IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Institute of Electrical and Electronics Engineers, 2004, pp.617--622. 〈10.1109/ICSMC.2004.1398368〉
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Nicolas Gourier, Daniela Hall, James L. Crowley. Facial features detection robust to pose, illumination and identity. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Institute of Electrical and Electronics Engineers, 2004, pp.617--622. 〈10.1109/ICSMC.2004.1398368〉. 〈hal-01253456〉

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