Joint Unsupervised Face Alignment and Behaviour Analysis^*

Lazaros Zafeiriou, Epameinondas Antonakos, Stefanos Zafeiriou, and Maja Pantic

Abstract. 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.

Keywords: Face alignment, time series alignment, slow feature analysis

Electronic Supplementary Material:

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LNCS 8692, p. 167 ff.

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