Detección Automática de Puntos faciales
El artículo presenta un método para detección de puntos en expresiones faciales usando el conjunto de imágenes Cohn-Kanade extendido. Para ello, se eligieron 334 imágenes de la base faces con las 6 expresiones faciales: alegría, tristeza, sorpresa, miedo, ira, asco y pose neutral. Se emplea el algor...
- Autores:
-
Ramos Almeida, Daniela
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Militar Nueva Granada
- Repositorio:
- Repositorio UMNG
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unimilitar.edu.co:10654/18038
- Acceso en línea:
- http://hdl.handle.net/10654/18038
- Palabra clave:
- EXPRESION FACIAL
ALGORITMOS (COMPUTADORES)
Facial Expression
HOG oriented gradient histogram
Viola Jones algorithm
Classification
Facial Points
Segmentation
Expresiones faciales
HOG histograma de gradientes orientados
algoritmo de Viola Jones
Clasificación
puntos faciales
segmentación
- Rights
- License
- Derechos Reservados - Universidad Militar Nueva Granada, 2018
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| dc.title.spa.fl_str_mv |
Detección Automática de Puntos faciales |
| dc.title.translated.spa.fl_str_mv |
Automatic Detection of Facial Points |
| title |
Detección Automática de Puntos faciales |
| spellingShingle |
Detección Automática de Puntos faciales EXPRESION FACIAL ALGORITMOS (COMPUTADORES) Facial Expression HOG oriented gradient histogram Viola Jones algorithm Classification Facial Points Segmentation Expresiones faciales HOG histograma de gradientes orientados algoritmo de Viola Jones Clasificación puntos faciales segmentación |
| title_short |
Detección Automática de Puntos faciales |
| title_full |
Detección Automática de Puntos faciales |
| title_fullStr |
Detección Automática de Puntos faciales |
| title_full_unstemmed |
Detección Automática de Puntos faciales |
| title_sort |
Detección Automática de Puntos faciales |
| dc.creator.fl_str_mv |
Ramos Almeida, Daniela |
| dc.contributor.advisor.spa.fl_str_mv |
Sierra, Eduard Leonardo |
| dc.contributor.author.spa.fl_str_mv |
Ramos Almeida, Daniela |
| dc.contributor.other.spa.fl_str_mv |
Sánchez, Wilman Helioth |
| dc.subject.lemb.spa.fl_str_mv |
EXPRESION FACIAL ALGORITMOS (COMPUTADORES) |
| topic |
EXPRESION FACIAL ALGORITMOS (COMPUTADORES) Facial Expression HOG oriented gradient histogram Viola Jones algorithm Classification Facial Points Segmentation Expresiones faciales HOG histograma de gradientes orientados algoritmo de Viola Jones Clasificación puntos faciales segmentación |
| dc.subject.keywords.spa.fl_str_mv |
Facial Expression HOG oriented gradient histogram Viola Jones algorithm Classification Facial Points Segmentation |
| dc.subject.proposal.spa.fl_str_mv |
Expresiones faciales HOG histograma de gradientes orientados algoritmo de Viola Jones Clasificación puntos faciales segmentación |
| description |
El artículo presenta un método para detección de puntos en expresiones faciales usando el conjunto de imágenes Cohn-Kanade extendido. Para ello, se eligieron 334 imágenes de la base faces con las 6 expresiones faciales: alegría, tristeza, sorpresa, miedo, ira, asco y pose neutral. Se emplea el algoritmo de Viola-Jones para extraer la región de interés del rostro, se identifica la expresión asociada al rostro. Cuando se identifican las expresiones se buscan 21 puntos faciales aproximados a partir de segmentaciones de las partes del rostro. Se presenta las bases teóricas, el desarrollo y los resultados del método. |
| publishDate |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2018-10-05T15:07:13Z 2019-12-26T22:04:54Z |
| dc.date.available.none.fl_str_mv |
2018-10-05T15:07:13Z 2019-12-26T22:04:54Z |
| dc.date.issued.none.fl_str_mv |
2018-09-20 |
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info:eu-repo/semantics/bachelorThesis |
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http://purl.org/coar/resource_type/c_7a1f |
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Trabajo de grado |
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Text |
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http://hdl.handle.net/10654/18038 |
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http://hdl.handle.net/10654/18038 |
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spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
M. Usman, S. Latif y J. Qadir, "Using deep autoencoders for facial expression recognition," 2017 13th International Conference on Emerging Technologies (ICET), Islamabad, 2017, pp. 1-6. doi: 10.1109/ICET.2017.8281753. D. I. S. Saputra y K. M. Amin, "Face detection and tracking using live video acquisition in camera closed circuit television and webcam," 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, 2016, pp. 154-157. doi: 10.1109/ICITISEE.2016.7803065 M. Owayjan, A. Kashour, N. Al Haddad, M. Fadel y G. Al Souki, "The design and development of a Lie Detection System using facial micro- expressions," 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), Beirut, 2012, pp. 33-38. doi: 10.1109/ICTEA.2012.6462897 U. Martinez-Hernandez y T. J. Prescott, "Expressive touch: Control of robot emotional expression by touch," 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO- MAN), New York, NY, 2016, pp. 974-979.doi: 10.1109/ROMAN.2016.7745227 M. D. Samad, N. Diawara, J. L. Bobzien, J.W. Harrington, M. A. Witherow, y K. M. Iftekharuddin "A Feasibility Study of Autism Behavioral Markers in Spontaneous Facial, Visual, and Hand Movement Response Data", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 353-361, 2018. doi: 10.1109/TNSRE.2017.276848 Y. Kumar y S. Sharma, "A systematic survey of facial expression recognition techniques" 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2017, pp. 1074-1079. doi: 10.1109/ICCMC.2017.8282636 A. Majumder, L. Behera y V. K. Subramanian, "Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion", IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 103-114, 2018. P. Ekman y W. Friesen, "Constants across cultures in the face and emotion.", Journal of Personality and Social Psychology, vol. 17, no. 2, pp. 124-129, 1971. G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman y T. J. Sejnowski, "Classifying facial actions", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 974-989, 1999. doi: 10.1109/34.799905. D. A. R. Wati y D. Abadianto, "Design of face detection and recognition system for smart home security application," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, 2017, pp. 342-347. doi: 10.1109/ICITISEE.2017.8285524 A. Micheal y P. Geetha, "Multi-view face detection using Normalized Pixel Difference feature," 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, 2017, pp. 0988-0992. doi: 10.1109/ICCSP.2017.8286520. L. Cuimei, Q. Zhiliang, J. Nan y W. Jianhua, "Human face detection algorithm via Haar cascade classifier combined with three additional classifiers," 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Yangzhou, 2017, pp. 483-487. doi: 10.1109/ICEMI.2017.8265863 M. Dahmane y J. Meunier, "Emotion recognition using dynamic grid- based HoG features," Face and Gesture 2011, Santa Barbara, CA, 2011, pp. 884-888. doi: 10.1109/FG.2011.5771368. H. Candra, M. Yuwono, R. Chai, H. T. Nguyen, y S. Su, "Classification of facial-emotion expression in the application of psychotherapy using Viola-Jones and Edge-Histogram of Oriented Gradient," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 423-426. doi: 10.1109/EMBC.2016.7590730 S. L. Phung and A. Bouzerdoum, "A Pyramidal Neural Network For Visual Pattern Recognition," in IEEE Transactions on Neural Networks, vol. 18, no. 2, pp. 329-343, March 2007. doi: 10.1109/TNN.2006.884677 D. Cherifi, F. Cherfaoui, S. Yacini y A. Nait-Ali, "Fusion of face recognition methods at score level," 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART), Dubai, 2016, pp. 1-5. doi: 10.1109/BIOSMART.2016.7835458 J. Zhu y Z. Chen, "Real Time Face Detection System Using Adaboost and Haar-like Features," 2015 2nd International Conference on Information Science and Control Engineering, Shanghai, 2015, pp. 404- 407. doi: 10.1109/ICISCE.2015.95 P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, y I. Matthews, The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression. Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA, 2010, pp. 94- 101. H. Yang y A. Wang, "Cascade Face Detection Based on Histograms of Oriented Gradients and Support Vector Machine," 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Krakow, 2015, pp. 766-770. doi: 10.1109/3PGCIC.2015.14 A. Sharifara , M. Mohd Rahim y Y. Anisi, "A general review of human face detection including a study of neural networks and Haar feature- based cascade classifier in face detection," 2014 International Symposium on Biometrics and Security Technologies (ISBAST), Kuala Lumpur, 2014, pp. 73-78. doi: 10.1109/ISBAST.2014.7013097 P. Carcagnì, M. Del Coco, C. Distante and M. Leo, "Facial expression recognition and histograms of oriented gradients: a comprehensive study", SpringerPlus, vol. 4, no. 1, 2015. Dalal, N. & Triggs, B. Histograms of oriented gradients for human detection 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, 1, 886-893 vol. 1 Canny, J. A Computational Approach to Edge Detection IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI- 8, 679-698 |
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Sierra, Eduard LeonardoRamos Almeida, DanielaIngeniero MultimediaSánchez, Wilman HeliothCalle 1002018-10-05T15:07:13Z2019-12-26T22:04:54Z2018-10-05T15:07:13Z2019-12-26T22:04:54Z2018-09-20http://hdl.handle.net/10654/18038El artículo presenta un método para detección de puntos en expresiones faciales usando el conjunto de imágenes Cohn-Kanade extendido. Para ello, se eligieron 334 imágenes de la base faces con las 6 expresiones faciales: alegría, tristeza, sorpresa, miedo, ira, asco y pose neutral. Se emplea el algoritmo de Viola-Jones para extraer la región de interés del rostro, se identifica la expresión asociada al rostro. Cuando se identifican las expresiones se buscan 21 puntos faciales aproximados a partir de segmentaciones de las partes del rostro. Se presenta las bases teóricas, el desarrollo y los resultados del método.Universidad Militar Nueva Granada1. Introducción, 2. Materiales y Métodos, 3. Resultados, 4.Conclusiones.This article develops a method for detection of facial landmarks in facial expressions using the Cohn-Kanade extended image base. For this, first it took images of 334 faces with the 6 facial expressions: joy, disgust, anger, fear, surprise, sadness and neutral pose. By means of the Viola-Jones algorithm the region of interest of the face was extracted. This region is classified in a expression. Once the classification of the expression is done, 21 facial points are calculated by means of a segmentation method of facial components. The present report shows the theoretical basis, the development, and the results of the method.Pregradoapplication/pdfspaDerechos Reservados - Universidad Militar Nueva Granada, 2018https://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadashttp://purl.org/coar/access_right/c_abf2Detección Automática de Puntos facialesAutomatic Detection of Facial Pointsinfo:eu-repo/semantics/bachelorThesisTrabajo de gradoTexthttp://purl.org/coar/resource_type/c_7a1fEXPRESION FACIALALGORITMOS (COMPUTADORES)Facial ExpressionHOG oriented gradient histogramViola Jones algorithmClassificationFacial PointsSegmentationExpresiones facialesHOG histograma de gradientes orientadosalgoritmo de Viola JonesClasificaciónpuntos facialessegmentaciónFacultad de IngenieríaIngeniería MultimediaIngeniería - Ingeniería en MultimediaUniversidad Militar Nueva GranadaM. Usman, S. Latif y J. Qadir, "Using deep autoencoders for facial expression recognition," 2017 13th International Conference on Emerging Technologies (ICET), Islamabad, 2017, pp. 1-6. doi: 10.1109/ICET.2017.8281753.D. I. S. Saputra y K. M. Amin, "Face detection and tracking using live video acquisition in camera closed circuit television and webcam," 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, 2016, pp. 154-157. doi: 10.1109/ICITISEE.2016.7803065M. Owayjan, A. Kashour, N. Al Haddad, M. Fadel y G. Al Souki, "The design and development of a Lie Detection System using facial micro- expressions," 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), Beirut, 2012, pp. 33-38. doi: 10.1109/ICTEA.2012.6462897U. Martinez-Hernandez y T. J. Prescott, "Expressive touch: Control of robot emotional expression by touch," 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO- MAN), New York, NY, 2016, pp. 974-979.doi: 10.1109/ROMAN.2016.7745227M. D. Samad, N. Diawara, J. L. Bobzien, J.W. Harrington, M. A. Witherow, y K. M. Iftekharuddin "A Feasibility Study of Autism Behavioral Markers in Spontaneous Facial, Visual, and Hand Movement Response Data", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 353-361, 2018. doi: 10.1109/TNSRE.2017.276848Y. Kumar y S. Sharma, "A systematic survey of facial expression recognition techniques" 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2017, pp. 1074-1079. doi: 10.1109/ICCMC.2017.8282636A. Majumder, L. Behera y V. K. Subramanian, "Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion", IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 103-114, 2018.P. Ekman y W. Friesen, "Constants across cultures in the face and emotion.", Journal of Personality and Social Psychology, vol. 17, no. 2, pp. 124-129, 1971.G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman y T. J. Sejnowski, "Classifying facial actions", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 974-989, 1999. doi: 10.1109/34.799905.D. A. R. Wati y D. Abadianto, "Design of face detection and recognition system for smart home security application," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, 2017, pp. 342-347. doi: 10.1109/ICITISEE.2017.8285524A. Micheal y P. Geetha, "Multi-view face detection using Normalized Pixel Difference feature," 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, 2017, pp. 0988-0992. doi: 10.1109/ICCSP.2017.8286520.L. Cuimei, Q. Zhiliang, J. Nan y W. Jianhua, "Human face detection algorithm via Haar cascade classifier combined with three additional classifiers," 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Yangzhou, 2017, pp. 483-487. doi: 10.1109/ICEMI.2017.8265863M. Dahmane y J. Meunier, "Emotion recognition using dynamic grid- based HoG features," Face and Gesture 2011, Santa Barbara, CA, 2011, pp. 884-888. doi: 10.1109/FG.2011.5771368.H. Candra, M. Yuwono, R. Chai, H. T. Nguyen, y S. Su, "Classification of facial-emotion expression in the application of psychotherapy using Viola-Jones and Edge-Histogram of Oriented Gradient," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 423-426. doi: 10.1109/EMBC.2016.7590730S. L. Phung and A. Bouzerdoum, "A Pyramidal Neural Network For Visual Pattern Recognition," in IEEE Transactions on Neural Networks, vol. 18, no. 2, pp. 329-343, March 2007. doi: 10.1109/TNN.2006.884677D. Cherifi, F. Cherfaoui, S. Yacini y A. Nait-Ali, "Fusion of face recognition methods at score level," 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART), Dubai, 2016, pp. 1-5. doi: 10.1109/BIOSMART.2016.7835458J. Zhu y Z. Chen, "Real Time Face Detection System Using Adaboost and Haar-like Features," 2015 2nd International Conference on Information Science and Control Engineering, Shanghai, 2015, pp. 404- 407. doi: 10.1109/ICISCE.2015.95P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, y I. Matthews, The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression. Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA, 2010, pp. 94- 101.H. Yang y A. Wang, "Cascade Face Detection Based on Histograms of Oriented Gradients and Support Vector Machine," 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Krakow, 2015, pp. 766-770. doi: 10.1109/3PGCIC.2015.14A. Sharifara , M. Mohd Rahim y Y. Anisi, "A general review of human face detection including a study of neural networks and Haar feature- based cascade classifier in face detection," 2014 International Symposium on Biometrics and Security Technologies (ISBAST), Kuala Lumpur, 2014, pp. 73-78. doi: 10.1109/ISBAST.2014.7013097P. Carcagnì, M. Del Coco, C. Distante and M. Leo, "Facial expression recognition and histograms of oriented gradients: a comprehensive study", SpringerPlus, vol. 4, no. 1, 2015.Dalal, N. & Triggs, B. Histograms of oriented gradients for human detection 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, 1, 886-893 vol. 1Canny, J. A Computational Approach to Edge Detection IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI- 8, 679-698THUMBNAILRamosAlmeidaDaniela2018.pdf.jpgIM Thumbnailimage/jpeg5828http://repository.unimilitar.edu.co/bitstream/10654/18038/1/RamosAlmeidaDaniela2018.pdf.jpgd7b659866e8f3e83e502af0ecc282fd0MD51ORIGINALRamosAlmeidaDaniela2018.pdfArticulo Deteccion Puntosapplication/pdf707208http://repository.unimilitar.edu.co/bitstream/10654/18038/2/RamosAlmeidaDaniela2018.pdfdba467344e050ea4fb6ef52fc465d7e2MD52LICENSElicense.txttext/plain2915http://repository.unimilitar.edu.co/bitstream/10654/18038/3/license.txt755421b5a8b45ce61d1a5793576f9a78MD5310654/18038oai:repository.unimilitar.edu.co:10654/180382020-06-30 12:56:23.029Repositorio Institucional UMNGbibliodigital@unimilitar.edu.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 |