Pose normalization for eye gaze estimation and facial attribute description from still images

Egger, Bernhard and Schönborn, Sandro and Forster, Andreas and Vetter, Thomas.. (2014) Pose normalization for eye gaze estimation and facial attribute description from still images. In: Pattern recognition, 36th German Conference, GCPR 2014 : proceedings. Cham, pp. 317-327.

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Official URL: http://edoc.unibas.ch/dok/A6328767

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Our goal is to obtain an eye gaze estimation and a face description based on attributes (e.g. glasses, beard or thick lips) from still images. An attribute-based face description reflects human vocabulary and is therefore adequate as face description. Head pose and eye gaze play an important role in human interaction and are a key element to extract interaction information from still images. Pose variation is a major challenge when analyzing them. Most current approaches for facial image analysis are not explicitly pose-invariant. To obtain a pose-invariant representation, we have to account the three dimensional nature of a face. A 3D Morphable Model (3DMM) of faces is used to obtain a dense3D reconstruction of the face in the image. This Analysis-by-Synthesis approach provides model parameters which contain an explicit face description and a dense model to image correspondence. However, the fit is restricted to the model space and cannot explain all variations. Our model only contains straight gaze directions and lacks high detail textural features. To overcome this limitations, we use the obtained correspondence in a discriminative approach. The dense correspondence is used to extract a pose-normalized version of the input image. The warped image contains all information from the orignal image and preserves gaze and detailed textural information. On the pose-normalized representation we train a regression function to obtain gaze estimation and attribute description. We provide results for pose-invariant gaze estimation on still images on the UUlm Head Pose and Gaze Database and attribute description on the Multi-PIE database. To the best of our knowledge, this is the first pose-invariant approach to estimate gaze from unconstrained still images.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Ehemalige Einheiten Mathematik & Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Vetter, Thomas and Schönborn, Sandro and Morel, Andreas and Egger, Bernhard
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:31 Dec 2015 10:56
Deposited On:09 Jan 2015 09:25

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