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Adaptive contour fitting for pose-invariant 3D face shape reconstruction

: Qu, C.; Monari, Eduardo; Schuchert, Tobias; Beyerer, Jürgen

Volltext urn:nbn:de:0011-n-3643741 (5.6 MByte PDF)
MD5 Fingerprint: ad72d57fda4dc3a1ba5fd33b48ab75ea
Erstellt am: 17.11.2015

British Machine Vision Association -BMVA-:
26th British Machine Vision Conference (BMVC). Proceedings : 7–10 September, Swansea, UK
Durham: BMVA, 2015
ISBN: 1-901725-53-7
S. 87.1-87.12
British Machine Vision Conference (BMVC) <26, 2015, Swansea>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Direct reconstruction of 3D face shape—solely based on a sparse set of 2D feature points localized by a facial landmark detector—offers an automatic, efficient and illumination-invariant alternative to the conventional analysis-by-synthesis 3D Morphable Model (3DMM) fitting. In this paper, we propose a novel algorithm that addresses the inconsistent correspondence of 2D and 3D landmarks at the facial contour due to head pose and localization ambiguity along the edge. To facilitate dynamic correspondence while fitting, a small subset of 3D vertices that serves as the contour candidates is annotated
offline. During the fitting process, we employ the Levenberg-Marquardt Iterative Closest Point (LM-ICP) algorithm in combination with Distance Transform (DT) within the constrained domain, which allows for fast convergence and robust estimation of 3D face shape against pose variation. Superior evaluation results reported on ground truth 3D face scans over the state-of-the-art demonstrate the efficacy of the proposed method