AU  - Çakır, Demet
AU  - Supervisor: Kuşaf, Mehmet 
TI  - Facial expression recognision based on scale invariant feature transform
PY  - 2010///
CY  - Nicosia
PB  - Cyprus International University
KW  - Scale invariant feature transform
KW  - Ölçek değişmez özellik dönüşümü
KW  - Kullback Leibler Divergence
N1  - include references (55-59 p.); 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
N2  - '	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. '
ER  -