cvpr cvpr2013 cvpr2013-359 cvpr2013-359-reference knowledge-graph by maker-knowledge-mining
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Author: Akshay Asthana, Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic
Abstract: We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms stateof-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1second per image. To facilitate future comparisons, we release the MATLAB code1 and the pretrained models for research purposes.
[1] T. Albrecht, M. L ¨uthi, and T. Vetter. A statistical deformation prior for non-rigid image and shape registration. In CVPR, 2008. 2 333444445088 333444445 919