TY - GEN
T1 - Joint Unsupervised Face Alignment and Behaviour Analysis
AU - Zafeiriou, Lazaros
AU - Antonakos, Epameinondas
AU - Zafeiriou, Stefanos
AU - Pantic, Maja
N1 - eemcs-eprint-25814
PY - 2014/9
Y1 - 2014/9
N2 - The predominant strategy for facial expressions analysis and temporal analysis of facial events is the following: a generic facial landmarks tracker, usually trained on thousands of carefully annotated examples, is applied to track the landmark points, and then analysis is performed using mostly the shape and more rarely the facial texture. This paper challenges the above framework by showing that it is feasible to perform joint landmarks localization (i.e. spatial alignment) and temporal analysis of behavioural sequence with the use of a simple face detector and a simple shape model. To do so, we propose a new component analysis technique, which we call Autoregressive Component Analysis (ARCA), and we show how the parameters of a motion model can be jointly retrieved. The method does not require the use of any sophisticated landmark tracking methodology and simply employs pixel intensities for the texture representation.
AB - The predominant strategy for facial expressions analysis and temporal analysis of facial events is the following: a generic facial landmarks tracker, usually trained on thousands of carefully annotated examples, is applied to track the landmark points, and then analysis is performed using mostly the shape and more rarely the facial texture. This paper challenges the above framework by showing that it is feasible to perform joint landmarks localization (i.e. spatial alignment) and temporal analysis of behavioural sequence with the use of a simple face detector and a simple shape model. To do so, we propose a new component analysis technique, which we call Autoregressive Component Analysis (ARCA), and we show how the parameters of a motion model can be jointly retrieved. The method does not require the use of any sophisticated landmark tracking methodology and simply employs pixel intensities for the texture representation.
KW - HMI-HF: Human Factors
KW - EC Grant Agreement nr.: FP7/288235
KW - EWI-25814
KW - time series alignment
KW - METIS-310010
KW - EC Grant Agreement nr.: FP7/2007-2013
KW - Face alignment
KW - Slow feature analysis
KW - IR-95221
KW - EC Grant Agreement nr.: FP7/611153
U2 - 10.1007/978-3-319-10593-2_12
DO - 10.1007/978-3-319-10593-2_12
M3 - Conference contribution
SN - 978-3-319-10592-5
T3 - Lecture Notes in Computer Science
SP - 167
EP - 183
BT - Proceedings of the 13th European Conference on Computer Vision, ECCV 2014
PB - Springer
CY - Switzerland
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -