Facial expression recognition has become an active research topic in recent
years due to its applications in human computer interfaces and data-driven animation. In this thesis, we focus on the problem of how to e?ectively use domain,
temporal and categorical information of facial expressions to help computer understand human emotions. Over the past decades, many techniques (such as
neural networks, Gaussian processes, support vector machines, etc.) have been
applied to facial expression analysis. Recently graphical models have emerged as
a general framework for applying probabilistic models. They provide a natural
framework for describing the generative process of facial expressions. However,
these models often su?er from too many latent variables or too complex model
structures, which makes learning and inference di±cult. In this thesis, we will
try to analyze the deformation of facial expression by introducing some recently
developed graphical models (e.g. latent topic model) or improving the recognition
ability of some already widely used models (e.g. HMM).
In this thesis, we develop three di?erent graphical models with di?erent representational assumptions: categories being represented by prototypes, sets of
exemplars and topics in between. Our ¯rst model incorporates exemplar-based
representation into graphical models. To further improve computational e±-
ciency of the proposed model, we build it in a local linear subspace constructed
by principal component analysis. The second model is an extension of the recently
developed topic model by introducing temporal and categorical information into
Latent Dirichlet Allocation model. In our discriminative temporal topic model
(DTTM), temporal information is integrated by placing an asymmetric Dirichlet
prior over document-topic distributions. The discriminative ability is improved by
a supervised term weighting scheme. We describe the resulting DTTM in detail
and show how it can be applied to facial expression recognition. Our third model
is a nonparametric discriminative variation of HMM. HMM can be viewed as a
prototype model, and transition parameters act as the prototype for one category.
To increase the discrimination ability of HMM at both class level and state level,
we introduce linear interpolation with maximum entropy (LIME) and member-
ship coe±cients to HMM. Furthermore, we present a general formula for output
probability estimation, which provides a way to develop new HMM. Experimental
results show that the performance of some existing HMMs can be improved by
integrating the proposed nonparametric kernel method and parameters adaption
formula.
In conclusion, this thesis develops three di?erent graphical models by (i) combining exemplar-based model with graphical models, (ii) introducing temporal
and categorical information into Latent Dirichlet Allocation (LDA) topic model,
and (iii) increasing the discrimination ability of HMM at both hidden state level
and class level. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/174506 |
| Date | January 2012 |
| Creators | Shang, Lifeng., 尚利峰. |
| Contributors | Chan, KP |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B47849484 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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