Spatio-temporal Representation and Analysis of Facial Expressions with Varying Intensities
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Facial expressions convey a wealth of information about our feelings, personality and mental
state. In this thesis we seek efficient ways of representing and analysing facial expressions of
varying intensities. Firstly, we analyse state-of-the-art systems by decomposing them into their
fundamental components, in an effort to understand what are the useful practices common to
successful systems. Secondly, we address the problem of sequence registration, which emerged
as an open issue in our analysis. The encoding of the (non-rigid) motions generated by facial expressions
is facilitated when the rigid motions caused by irrelevant factors, such as camera movement,
are eliminated. We propose a sequence registration framework that is based on pre-trained
regressors of Gabor motion energy. Comprehensive experiments show that the proposed method
achieves very high registration accuracy even under difficult illumination variations. Finally,
we propose an unsupervised representation learning framework for encoding the spatio-temporal
evolution of facial expressions. The proposed framework is inspired by the Facial Action Coding
System (FACS), which predates computer-based analysis. FACS encodes an expression in terms
of localised facial movements and assigns an intensity score for each movement. The framework
we propose mimics those two properties of FACS. Specifically, we propose to learn from
data a linear transformation that approximates the facial expression variation in a sequence as
a weighted sum of localised basis functions, where the weight of each basis function relates to
movement intensity. We show that the proposed framework provides a plausible description of
facial expressions, and leads to state-of-the-art performance in recognising expressions across
intensities; from fully blown expressions to micro-expressions.
Authors
Sariyanidi, EvangelosCollections
- Theses [4189]