Data Driven Analysis of Faces from Images

dc.contributor.authorScherbaum, Kristinaen_US
dc.coverage.spatialUniversität des Saarlandes, Germanyen_US
dc.date.accessioned2015-01-21T06:56:21Z
dc.date.available2015-01-21T06:56:21Z
dc.date.issued2013-09-17en_US
dc.description.abstractThis thesis proposes three new data-driven approaches to detect, analyze, or modify faces in images. All presented contributions are inspired by the use of prior knowledge and they derive information about facial appearances from pre-collected databases of images or 3D face models. First, we contribute an approach that extends a widely-used monocular face detector by an additional classifier that evaluates disparity maps of a passive stereo camera. The algorithm runs in real-time and significantly reduces the number of false positives compared to the monocular approach. Next, with a many-core implementation of the detector, we train view-dependent face detectors based on tailored views which guarantee that the statistical variability is fully covered. These detectors are superior to the state of the art on a challenging dataset and can be trained in an automated procedure. Finally, we contribute a model describing the relation of facial appearance and makeup. The approach extracts makeup from before/after images of faces and allows to modify faces in images. Applications such as machine-suggested makeup can improve perceived attractiveness as shown in a perceptual study. In summary, the presented methods help improve the outcome of face detection algorithms, ease and automate their training procedures and the modification of faces in images. Moreover, their data-driven nature enables new and powerful applications arising from the use of prior knowledge and statistical analyses.en_US
dc.formatapplication/pdfen_US
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/8316
dc.languageEnglishen_US
dc.publisherScherbaum, Kristinaen_US
dc.titleData Driven Analysis of Faces from Imagesen_US
dc.typeText.PhDThesisen_US
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