Facial expression recognision based on scale invariant feature transform Demet Çakır; Supervisor: Mehmet Kuşaf

Yazar: Katkıda bulunan(lar):Dil: İngilizce Yayın ayrıntıları:Nicosia Cyprus International University 2010Tanım: X, 59 p ill, fig 30.5 cmİçerik türü:
  • text
Ortam türü:
  • unmediated
Taşıyıcı türü:
  • volume
Konu(lar):
Eksik içerik
1 CHAPTER 1-INTRODUCTION
1 Overview
2 Problem Statement and Its Significance
4 Literature Survey
5 Geometric Features
6 Apperance Features
9 Model Template-Based Approaches
11 Local Descriptors
12 Contribution to Facial Representaion
12 Feature Representation
13 Feature Extraction and Selection
13 Classifier Design
14 Thesis Overview
16 CHAPTER 2-SIFT METHODOLOGY
16 Scale-Space Extreme Detection
19 Keypoint Localization
21 Keypoint Descriptor
24 CHAPTER 3-FEATURE REPRESENTATION FOR MATCHING
24 Basic Concepts of Feature Representation
24 SIFT Feature Representation using Face Descriptors
26 Feature Matching
27 Face Descriptor Matching
29 Improved SIFT Matching Using Kullback-Leibler Divergence
30 Evaluation and Experiment Results
32 Conclusions
33 CHAPTER 4: FEATURE EXTRACTION AND SELECTION
33 Feature Selection
34 Feature Selection
34 Purpose and Importance of Feature Selection
35 Choosing among Features
36 Related Work
37 Discriminating Power Estimation of SIFT Features
38 BU-3DFE Database
39 Evaluation and Experiment Result
44 Conclusions
45 CHAPTER 5-CLASSIFIER DESIGN FOR SIFT FEATURES
45 Classifier Design
45 Image Matching using Regular SIFT
46 Minimum Pair Distance (MPD)
47 Sub-region Construction for Feature Matching
48 Regular Grid Base Face Descriptor Matching
49 Evaluation and Experimental Results
51 Conclusion
53 CHAPTER 6-CONCLUSION AND FUTURE WORKS
53 Conclusions
53 Future Works
55 REFERENCES
Özet: ' Human computer interaction (HCL) is an active research area since the early days of computers. HCL aims to ease computer usage through various methods. One of the most important method is to act on human behavior. In this problem of person independent facial expression recognition from Scale Invariant Features Transform (SIFT) is investigated. Since human communication is mainly performed by face, we have chosen to extract facial expressions. Our primary objective is to increase available identification performance in the literature, while trying to solve problems related to external facts, such as lighting orientation and scaling. In our research, we propose a feature selection methodology that uses key point descriptors of the SIFT features features extracted from the expression images to improve classifications. The selection algorithm aims to derive a set of SIFT features from the original expression images which minimizes the within-class variance. The proposed methodology is evaluated using 3D facial expression data base BU-3DFE (Binghamton University 3D Facial Expression). Kullback Leibler Divergence (KLD) is used as a distance measure for computing the dissimilar score between histograms. Facial expression such as anger, fear, happiness, sadness and surprise are successfully recognized. '
Materyal türü: Thesis

include references (55-59 p.)

' Human computer interaction (HCL) is an active research area since the early days of computers. HCL aims to ease computer usage through various methods. One of the most important method is to act on human behavior. In this problem of person independent facial expression recognition from Scale Invariant Features Transform (SIFT) is investigated. Since human communication is mainly performed by face, we have chosen to extract facial expressions. Our primary objective is to increase available identification performance in the literature, while trying to solve problems related to external facts, such as lighting orientation and scaling. In our research, we propose a feature selection methodology that uses key point descriptors of the SIFT features features extracted from the expression images to improve classifications. The selection algorithm aims to derive a set of SIFT features from the original expression images which minimizes the within-class variance. The proposed methodology is evaluated using 3D facial expression data base BU-3DFE (Binghamton University 3D Facial Expression). Kullback Leibler Divergence (KLD) is used as a distance measure for computing the dissimilar score between histograms. Facial expression such as anger, fear, happiness, sadness and surprise are successfully recognized. '

1 CHAPTER 1-INTRODUCTION

1 Overview

2 Problem Statement and Its Significance

4 Literature Survey

5 Geometric Features

6 Apperance Features

9 Model Template-Based Approaches

11 Local Descriptors

12 Contribution to Facial Representaion

12 Feature Representation

13 Feature Extraction and Selection

13 Classifier Design

14 Thesis Overview

16 CHAPTER 2-SIFT METHODOLOGY

16 Scale-Space Extreme Detection

19 Keypoint Localization

21 Keypoint Descriptor

24 CHAPTER 3-FEATURE REPRESENTATION FOR MATCHING

24 Basic Concepts of Feature Representation

24 SIFT Feature Representation using Face Descriptors

26 Feature Matching

27 Face Descriptor Matching

29 Improved SIFT Matching Using Kullback-Leibler Divergence

30 Evaluation and Experiment Results

32 Conclusions

33 CHAPTER 4: FEATURE EXTRACTION AND SELECTION

33 Feature Selection

34 Feature Selection

34 Purpose and Importance of Feature Selection

35 Choosing among Features

36 Related Work

37 Discriminating Power Estimation of SIFT Features

38 BU-3DFE Database

39 Evaluation and Experiment Result

44 Conclusions

45 CHAPTER 5-CLASSIFIER DESIGN FOR SIFT FEATURES

45 Classifier Design

45 Image Matching using Regular SIFT

46 Minimum Pair Distance (MPD)

47 Sub-region Construction for Feature Matching

48 Regular Grid Base Face Descriptor Matching

49 Evaluation and Experimental Results

51 Conclusion

53 CHAPTER 6-CONCLUSION AND FUTURE WORKS

53 Conclusions

53 Future Works

55 REFERENCES

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