Face Recognition

Face Recognition

Daijin Kim (Pohang University of Science & Technology, Korea) and Jaewon Sung (LG Electronics, Korea)
Copyright: © 2009 |Pages: 92
DOI: 10.4018/978-1-60566-216-9.ch005
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Abstract

In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected to replace traditional authentication methods that are easily stolen, forgotten and duplicated. Fingerprints, face, iris, and voiceprints are commonly used biometric features. Among these features, face provides a more direct, friendly and convenient identification method and is more acceptable compared with the individual identification methods of other biometrics features. Thus, face recognition is one of the most important parts in biometrics.
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5.2 Embedded Hidden Markov Model

An embedded hidden Markov model (EHMM) is an extension of the previous 1-D HMM in order to deal with two-dimensional data such as images and videos. The EHMM was previously used for character recognition by Kuo and Agazzi (Kuo and Agazzi, 1994). In fact, a fully connected 2-D HMM would be desirable for modelling a 2-D face image. However, the computational complexity for a fully connected 2-D HMM increases explosively as the number of states grows. In this work, we adopt the Samaria's Pseudo 2-D HMM (P2D-HMM) obtained by linking 1-D left-right HMMs to form super states. The structure includes one internal 1-D HMM in each state in the outer 1-D HMM. While transitions in a vertical direction are allowed between two adjacent super states which include the super state itself, transitions in a horizontal direction are only allowed between two adjacent states in a super state including the state itself. From this structure, we know that the sequence of super states are used to model a horizontal slice of face image along the vertical direction and that the sequence of states in a super state are used to model a block image along the horizontal direction. We call this model an embedded HMM. It differs from a 2-D HMM since a transition between the states in different states are allowed. Figure 1 illustrates a 5-super states EHMM for face modelling, where each super state represents vertical facial features, such as forehead, eyes, nose, mouth, and chin in the face and each state in the super state represents horizontal local block features in the corresponding facial feature.

Figure 1.

An illustration of 2-D EHMM with 5 super states for face modeling

The EHMM is defined by specifying the following parameters (Samaria and Young, 1994):

  • N0 is the number of super states in the vertical direction of EHMM.

  • is the initial super state distribution, where π0,i is the probability of observation sequence being in the ith super state.

  • is the super state transition probability matrix, where is the probability of transition from the ith to the jth super state.

  • is the set of left-right 1-D HMMs in each super state. Each is specified by the standard 1-D HMM parameters as follows.

    • -

      is the number of states in the kth super state.

    • -

      is the initial state distribution, where is the probability of observation being in the jth state of the kth super state.

    • -

      is the state transition probability matrix, where is the probability of transition from the ith to the jth state of the kth super state.

    • -

      is the output probability function, where

Complete Chapter List

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Table of Contents
Acknowledgment
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
About the Authors