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005 | 20221103135316.0 | ||
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040 |
_aCY-NiCIU _btur _cCY-NiCIU _erda |
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041 | _aeng | ||
090 |
_aYL 287 _bC25 2010 |
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100 | _aÇakır, Demet | ||
245 |
_aFacial expression recognision based on scale invariant feature transform _cDemet Çakır; Supervisor: Mehmet Kuşaf |
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260 |
_aNicosia _bCyprus International University _c2010 |
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300 |
_aX, 59 p _bill, fig _c30.5 cm |
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336 |
_2rdacontent _atext _btxt |
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337 |
_2rdamedia _aunmediated _bn |
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_2rdacarrier _avolume _bnc |
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504 | _ainclude references (55-59 p.) | ||
520 | _a' 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. ' | ||
650 | _aScale invariant feature transform | ||
650 | _aÖlçek değişmez özellik dönüşümü | ||
650 | _aKullback Leibler Divergence | ||
650 | _aKullback Leibler Divergence | ||
700 |
_aSupervisor: Kuşaf, Mehmet _91656 |
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942 |
_2ddc _cTS |
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505 | 1 |
_g1 _tCHAPTER 1-INTRODUCTION |
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505 | 1 |
_g1 _tOverview |
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505 | 1 |
_g2 _tProblem Statement and Its Significance |
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505 | 1 |
_g4 _tLiterature Survey |
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505 | 1 |
_g5 _tGeometric Features |
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505 | 1 |
_g6 _tApperance Features |
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505 | 1 |
_g9 _tModel Template-Based Approaches |
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505 | 1 |
_g11 _tLocal Descriptors |
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505 | 1 |
_g12 _tContribution to Facial Representaion |
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505 | 1 |
_g12 _tFeature Representation |
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505 | 1 |
_g13 _tFeature Extraction and Selection |
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505 | 1 |
_g13 _tClassifier Design |
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505 | 1 |
_g14 _tThesis Overview |
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505 | 1 |
_g16 _tCHAPTER 2-SIFT METHODOLOGY |
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505 | 1 |
_g16 _tScale-Space Extreme Detection |
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505 | 1 |
_g19 _tKeypoint Localization |
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505 | 1 |
_g21 _tKeypoint Descriptor |
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505 | 1 |
_g24 _tCHAPTER 3-FEATURE REPRESENTATION FOR MATCHING |
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505 | 1 |
_g24 _tBasic Concepts of Feature Representation |
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505 | 1 |
_g24 _tSIFT Feature Representation using Face Descriptors |
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505 | 1 |
_g26 _tFeature Matching |
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505 | 1 |
_g27 _tFace Descriptor Matching |
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505 | 1 |
_g29 _tImproved SIFT Matching Using Kullback-Leibler Divergence |
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505 | 1 |
_g30 _tEvaluation and Experiment Results |
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505 | 1 |
_g32 _tConclusions |
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505 | 1 |
_g33 _tCHAPTER 4: FEATURE EXTRACTION AND SELECTION |
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505 | 1 |
_g33 _tFeature Selection |
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505 | 1 |
_g34 _tFeature Selection |
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505 | 1 |
_g34 _tPurpose and Importance of Feature Selection |
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505 | 1 |
_g35 _tChoosing among Features |
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505 | 1 |
_g36 _tRelated Work |
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505 | 1 |
_g37 _tDiscriminating Power Estimation of SIFT Features |
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505 | 1 |
_g38 _tBU-3DFE Database |
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505 | 1 |
_g39 _tEvaluation and Experiment Result |
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505 | 1 |
_g44 _tConclusions |
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505 | 1 |
_g45 _tCHAPTER 5-CLASSIFIER DESIGN FOR SIFT FEATURES |
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505 | 1 |
_g45 _tClassifier Design |
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505 | 1 |
_g45 _tImage Matching using Regular SIFT |
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505 | 1 |
_g46 _tMinimum Pair Distance (MPD) |
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505 | 1 |
_g47 _tSub-region Construction for Feature Matching |
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505 | 1 |
_g48 _tRegular Grid Base Face Descriptor Matching |
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505 | 1 |
_g49 _tEvaluation and Experimental Results |
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505 | 1 |
_g51 _tConclusion |
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505 | 1 |
_g53 _tCHAPTER 6-CONCLUSION AND FUTURE WORKS |
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505 | 1 |
_g53 _tConclusions |
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505 | 1 |
_g53 _tFuture Works |
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505 | 1 |
_g55 _tREFERENCES |
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_c324 _d324 |