Facial features detection robust to pose, illumination and identity

Nicolas Gourier (1) , Daniela Hall (1) , James L. Crowley (2, 1, 3, 4)

1 PRIMA - Perception, recognition and integration for observation of activity
2 Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
3 UGA [2016-2019] - Université Grenoble Alpes [2016-2019]
4 Inria Grenoble - Rhône-Alpes

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.

Mots clés: Computer Vision; Face Analysis

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Interface homme-machine [cs.HC]; Robotique [cs.RO]; Informatique ubiquitaire

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, 2004, 1, pp.617--622. ⟨10.1109/ICSMC.2004.1398368⟩. ⟨hal-01253456⟩