Imperial College London


Faculty of EngineeringDepartment of Computing

Professor in Machine Learning & Computer Vision



+44 (0)20 7594 8461s.zafeiriou Website CV




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BibTex format

author = {Asthana, A and Zafeiriou, S and Cheng, S and Pantic, M},
doi = {10.1109/CVPR.2013.442},
pages = {3444--3451},
publisher = {IEEE},
title = {Robust Discriminative Response Map Fitting with Constrained Local Models},
url = {},
year = {2013}

RIS format (EndNote, RefMan)

AB - 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 state-of-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 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.
AU - Asthana,A
AU - Zafeiriou,S
AU - Cheng,S
AU - Pantic,M
DO - 10.1109/CVPR.2013.442
EP - 3451
PY - 2013///
SN - 1063-6919
SP - 3444
TI - Robust Discriminative Response Map Fitting with Constrained Local Models
UR -
UR -
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ER -