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Facial landmark detection in unconstrained environments

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If you have a question about this talk, please contact Jingjing Shen.

Automatic facial landmark detection is a longstanding problem in computer vision. Facial feature detection algorithms have seen great progress over the recent years. However, they still struggle in poor lighting conditions and in the presence of extreme pose or occlusions. I will present the Constrained Local Neural Field model for facial landmark detection that deals with some of these problems and demonstrates state of the art performance on a number of datasets. This is a practice talk for International Conference on Computer Vision, 300 faces in-the-wild challenge.

This talk is part of the Rainbow Group Seminars series.

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