Synopsis
The comparison between the methodologies used for human emotion recognition from face images based on textural analysis and KNN clas- sifier. Automatic facial expression recognition (FER) plays an impor- tant role in Human Computer Interaction (HCI) systems for measuring people’s emotions has dominated psychology by linking expressions to a group of basic emotions (i.e., anger, disgust, fear, happiness, sad- ness, and surprise).The comparative study of Facial Expression Recog- nition involves Curvelet transform based Robust Local Binary Pattern (RLBP) and Distinct LBP (DLBP) features and features derived from DLBP and GLCM. The objective of this research is to show that fea- tures derived from RLBP with DLBP is superior to the features de- rived from DLBP and GLCM. To test and evaluate their performance, experiments are performed using Japanese Female Expressions Model (JAFEE) database in both techniques. The comparison chart shows that, the DLBP and RLBP based feature extraction with KNN classi- fier gives much better accuracy than other existing methods.
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Facial Expression Recognition using Knn Classifier
Edité par
LAP LAMBERT Academic Publishing Apr 2020, 2020
ISBN 10 : 6202525274
ISBN 13 : 9786202525275
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The comparison between the methodologies used for human emotion recognition from face images based on textural analysis and KNN clas- sifier. Automatic facial expression recognition (FER) plays an impor- tant role in Human Computer Interaction (HCI) systems for measuring people's emotions has dominated psychology by linking expressions to a group of basic emotions (i.e., anger, disgust, fear, happiness, sad- ness, and surprise).The comparative study of Facial Expression Recog- nition involves Curvelet transform based Robust Local Binary Pattern (RLBP) and Distinct LBP (DLBP) features and features derived from DLBP and GLCM. The objective of this research is to show that fea- tures derived from RLBP with DLBP is superior to the features de- rived from DLBP and GLCM. To test and evaluate their performance, experiments are performed using Japanese Female Expressions Model (JAFEE) database in both techniques. The comparison chart shows that, the DLBP and RLBP based feature extraction with KNN classi- fier gives much better accuracy than other existing methods. 52 pp. Englisch. N° de réf. du vendeur 9786202525275
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Facial Expression Recognition using Knn Classifier
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LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202525274
ISBN 13 : 9786202525275
Neuf
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Pardeshi ShailendraMr. Shailendra M. Pardeshi working as assistant professor at computer engineering department, RCPIT, Shirpur,MH, INDIA. He has published various national & international conference & journal papers & books. Attende. N° de réf. du vendeur 385946233
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Facial Expression Recognition using Knn Classifier
Edité par
LAP LAMBERT Academic Publishing Apr 2020, 2020
ISBN 10 : 6202525274
ISBN 13 : 9786202525275
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The comparison between the methodologies used for human emotion recognition from face images based on textural analysis and KNN clas- sifier. Automatic facial expression recognition (FER) plays an impor- tant role in Human Computer Interaction (HCI) systems for measuring people's emotions has dominated psychology by linking expressions to a group of basic emotions (i.e., anger, disgust, fear, happiness, sad- ness, and surprise).The comparative study of Facial Expression Recog- nition involves Curvelet transform based Robust Local Binary Pattern (RLBP) and Distinct LBP (DLBP) features and features derived from DLBP and GLCM. The objective of this research is to show that fea- tures derived from RLBP with DLBP is superior to the features de- rived from DLBP and GLCM. To test and evaluate their performance, experiments are performed using Japanese Female Expressions Model (JAFEE) database in both techniques. The comparison chart shows that, the DLBP and RLBP based feature extraction with KNN classi- fier gives much better accuracy than other existing methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. N° de réf. du vendeur 9786202525275
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Facial Expression Recognition using Knn Classifier
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202525274
ISBN 13 : 9786202525275
Neuf
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The comparison between the methodologies used for human emotion recognition from face images based on textural analysis and KNN clas- sifier. Automatic facial expression recognition (FER) plays an impor- tant role in Human Computer Interaction (HCI) systems for measuring people's emotions has dominated psychology by linking expressions to a group of basic emotions (i.e., anger, disgust, fear, happiness, sad- ness, and surprise).The comparative study of Facial Expression Recog- nition involves Curvelet transform based Robust Local Binary Pattern (RLBP) and Distinct LBP (DLBP) features and features derived from DLBP and GLCM. The objective of this research is to show that fea- tures derived from RLBP with DLBP is superior to the features de- rived from DLBP and GLCM. To test and evaluate their performance, experiments are performed using Japanese Female Expressions Model (JAFEE) database in both techniques. The comparison chart shows that, the DLBP and RLBP based feature extraction with KNN classi- fier gives much better accuracy than other existing methods. N° de réf. du vendeur 9786202525275
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Facial Expression Recognition using Knn Classifier
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202525274
ISBN 13 : 9786202525275
Neuf
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Taschenbuch. Etat : Neu. Facial Expression Recognition using Knn Classifier | Shailendra Pardeshi | Taschenbuch | 52 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202525275 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 118373072
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