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Support vector machine Adaboost Linear discriminant analysis Linear programming a b s t r a c t Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human–computer interaction and data-driven animation. Deriving an effective facial representation from original face images is(More)
Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual(More)
Recovering the shape of any 3D object using multiple 2D views requires establishing correspondence between feature points at different views. However changes in viewpoint introduce self-occlusions, resulting nonlinear variations in the shape and inconsistent 2D features between views. Here we introduce a multi-view nonlinear shape model utilising 2D(More)
Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting , background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features(More)
Much of recent action recognition research is based on space-time interest points extracted from video using a Bag of Words (BOW) representation. It mainly relies on the dis-criminative power of individual local space-time descrip-tors, whilst ignoring potentially valuable information about the global spatio-temporal distribution of interest points. In this(More)
We develop a novel visual behaviour modelling approach that performs incremental and adaptive model learning for online abnormality detection in a visual surveillance scene. The approach has the following key features that make it advantageous over previous ones: (1) Fully unsupervised learning: both feature extraction for behaviour pattern representation(More)
A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple Local Binary Patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a(More)
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robust-ness and computational(More)