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Facial Gesture Recognition

Copyright © 2009. 8 pages.
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DOI: 10.4018/978-1-60566-216-9.ch007|
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MLA

Kim, Daijin and Jaewon Sung. "Facial Gesture Recognition." Automated Face Analysis: Emerging Technologies and Research. IGI Global, 2009. 318-325. Web. 22 Nov. 2014. doi:10.4018/978-1-60566-216-9.ch007

APA

Kim, D., & Sung, J. (2009). Facial Gesture Recognition. In D. Kim, & J. Sung (Eds.) Automated Face Analysis: Emerging Technologies and Research (pp. 318-325). Hershey, PA: Medical Information Science Reference. doi:10.4018/978-1-60566-216-9.ch007

Chicago

Kim, Daijin and Jaewon Sung. "Facial Gesture Recognition." In Automated Face Analysis: Emerging Technologies and Research, ed. Daijin Kim and Jaewon Sung, 318-325 (2009), accessed November 22, 2014. doi:10.4018/978-1-60566-216-9.ch007

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Abstract

From facial gestures, we can extract many kinds of messages in human communication: they represent visible speech signals and clarify whether our current focus of attention is important, funny or unpleasant for us. They are direct, naturally preeminent means for humans to communicate their emotions (Russell and Fernandez-Dols, 1997). Automatic analyzers of subtle facial changes, therefore, seem to have a natural place in various vision systems including automated tools for psychological research, lip reading, bimodal speech analysis, affective computing, face and visual-speech synthesis, and perceptual user interfaces.
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7.1 Hidden Markov Model

An HMM is a statistical modeling tool which is applicable to analyzing time-series with spatial and temporal variability (Lee et. al., 1999; Jordan, 2003; Duda et al, 2000). It is a graphical model that can be viewed as a dynamic mixture model whose mixture components are treated as states. It has been applied in classification and modeling problems such as speech or gesture recognition. Figure 1 illustrates a simple HMM structure.

Figure 1

HMM structure

The hidden Markov model (HMM) is extension of a Markov model, where each state generates an observation. We can extend the concept of Markov models to include the case where the observation is a probabilistic function of the state. The resulting model, called a hidden Markov model, is a doubly embedded stochastic process with an underlying stochastic process that is not observable, but can only be observed through another set of stochastic processes that produce the sequence of observations. The HMM model is usually exploited to investigate the time varying sequence of observations and is regarded as a special case of a Bayesian belief network because it can be used for a probabilistic model of causal dependencies between different states (Gong et. al., 2000). Figure 2 illustrates a 5-state (1-D) HMM used for face modelling.

Figure 2

An illustration of 1-D HMM with 5 states for face modeling

The HMM is defined by specifying the following parameters (Rabiner, 1989):

  • N: The number of states in the model. The individual states are denoted as S={S1,S2,⋯,SN} and the state of the model at time t is qt, qtS and 1≤tT, where T is the length of the output observable symbol sequence.

  • M: The number of distinct observable symbols. The individual symbols are denoted as V={v1,v2,⋯,vM}.

  • AN×N: An N×N matrix specifies the state-transition probability that the state will transit from state Si to state Sj. AN×N=[aij]1≤i,jN, where aij=P(qt+1=Sj|qt=Si.

  • BN×M: An N×M matrix specifies that the the system will generate the observable symbol vk at state Sj and at time t. BN×M=[bj(k)]1≤jN,1≤kM, where bj(k)=P(vkatt|qt=Sj).

  • πN: An N-element vector that indicates the initial state probabilities. πN=[πi]1≤iN, where πi=P(qt=Si).

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Complete Chapter List

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Table of Contents
Chapter 1
Introduction  (pages 1-4)
Daijin Kim, Jaewon Sung
Communication between one human and another is the hallmark of our species. According to neuropsychology, the human face is the primary tool in... Sample PDF
Introduction
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Chapter 2
Daijin Kim, Jaewon Sung
Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face... Sample PDF
Face and Eye Detection
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Chapter 3
Face Modeling  (pages 45-91)
Daijin Kim, Jaewon Sung
In the field of computer vision, researchers have proposed many techniques for representation and analysis of the varying shape of objects, such as... Sample PDF
Face Modeling
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Chapter 4
Face Tracking  (pages 92-162)
Daijin Kim, Jaewon Sung
When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the... Sample PDF
Face Tracking
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Chapter 5
Face Recognition  (pages 163-254)
Daijin Kim, Jaewon Sung
In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected... Sample PDF
Face Recognition
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Chapter 6
Daijin Kim, Jaewon Sung
The facial expression has long been an interest for psychology, since Darwin published The expression of Emotions in Man and Animals (Darwin, C.... Sample PDF
Facial Expression Recognition
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Chapter 7
Daijin Kim, Jaewon Sung
From facial gestures, we can extract many kinds of messages in human communication: they represent visible speech signals and clarify whether our... Sample PDF
Facial Gesture Recognition
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Chapter 8
Human Motion Analysis  (pages 326-397)
Daijin Kim, Jaewon Sung
Human motion analysis (Moeslund et. al., 2006; Wang et. al., 2003) is currently one of the most active research areas in computer vision due both to... Sample PDF
Human Motion Analysis
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Abbreviations and Important Symbols