Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar
Este documento presenta el desarrollo de un algoritmo de re-identificación multimodal para mejorar la interacción Humano-Robot en el ámbito de asistencia doméstica. De esta manera, se integraron diferentes estrategias de reconocimiento de personas como lo son reconocimiento facial, por voz y por car...
- Autores:
-
Plazas Pirabán, Lina Alejandra
Betancur Sanchez, Bryan Steven
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/35563
- Acceso en línea:
- http://hdl.handle.net/11634/35563
- Palabra clave:
- Face recognition
Voice recognition
Soft-biometric characteristics
Inteligencia artificial
Deep learning
Algoritmo multimodal
Reconocimiento rostro
Reconocimiento voz
Características soft-biométricas
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
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Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| title |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| spellingShingle |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar Face recognition Voice recognition Soft-biometric characteristics Inteligencia artificial Deep learning Algoritmo multimodal Reconocimiento rostro Reconocimiento voz Características soft-biométricas |
| title_short |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| title_full |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| title_fullStr |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| title_full_unstemmed |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| title_sort |
Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar |
| dc.creator.fl_str_mv |
Plazas Pirabán, Lina Alejandra Betancur Sanchez, Bryan Steven |
| dc.contributor.advisor.none.fl_str_mv |
Mateus Rojas, Armando |
| dc.contributor.author.none.fl_str_mv |
Plazas Pirabán, Lina Alejandra Betancur Sanchez, Bryan Steven |
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https://orcid.org/0000-0002-2399-4859 |
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https://scholar.google.com/citations?user=1az5o_IAAAAJ&hl=es |
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https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000680630 |
| dc.contributor.corporatename.spa.fl_str_mv |
Universidad Santo Tomás |
| dc.subject.keyword.spa.fl_str_mv |
Face recognition Voice recognition Soft-biometric characteristics |
| topic |
Face recognition Voice recognition Soft-biometric characteristics Inteligencia artificial Deep learning Algoritmo multimodal Reconocimiento rostro Reconocimiento voz Características soft-biométricas |
| dc.subject.lemb.spa.fl_str_mv |
Inteligencia artificial Deep learning Algoritmo multimodal |
| dc.subject.proposal.spa.fl_str_mv |
Reconocimiento rostro Reconocimiento voz Características soft-biométricas |
| description |
Este documento presenta el desarrollo de un algoritmo de re-identificación multimodal para mejorar la interacción Humano-Robot en el ámbito de asistencia doméstica. De esta manera, se integraron diferentes estrategias de reconocimiento de personas como lo son reconocimiento facial, por voz y por características soft-biométricas (color de cabello, ojos y piel). Para esto, en primer lugar se realizó una consulta bibliográfica donde se eligieron posibles algoritmos a utilizar; luego se implementaron y se realizaron diferentes pruebas con el fin de elegir los algoritmos que presentaban mejores resultados por cada estrategia de re-identificación, después se integraron en un único desarrollo basado en regresión lineal múltiple el cual tuvo un porcentaje de acierto del 97.4%. De igual manera, se implementó todo el sistema en ROS (sistema operativo robótico) y se realizaron pruebas donde se evaluó si el algoritmo reconocía órdenes básicas personalizadas. |
| publishDate |
2021 |
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2021-09-17T15:04:03Z |
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2021-09-17T15:04:03Z |
| dc.date.issued.none.fl_str_mv |
2021-09-16 |
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Trabajo de grado |
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info:eu-repo/semantics/acceptedVersion |
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Formación de Recurso Humano para la Ctel: Trabajo de grado de Pregrado |
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http://purl.org/coar/resource_type/c_7a1f |
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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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Plazas Pirabán, L.A. & Betancur Sanchez, B.S. (2021) Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar [Trabajo de grado pregrado Ingeniería Electrónica] Repositorio Institucional |
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http://hdl.handle.net/11634/35563 |
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reponame:Repositorio Institucional Universidad Santo Tomás |
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repourl:https://repository.usta.edu.co |
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Plazas Pirabán, L.A. & Betancur Sanchez, B.S. (2021) Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar [Trabajo de grado pregrado Ingeniería Electrónica] Repositorio Institucional reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co |
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Alpaydin E. Introduction to machine learning. MIT press; 2020. Kapusta A, Erickson Z, Clever HM, et al. Personalized collaborative plans for robot-assisted dressing via optimization and simulation. Autonomous Robots. 2019;43(8):2183-2207. Zhang DD. Automated biometrics: Technologies and systems. Vol 7. Springer Science & Business Media; 2013. Alejandra MD. Re-identificaci´on de personas a trav´es de sus caracter´ısticas soft-biom´etricas en un entorno multi-c´amara de video vigilancia. Ingenier´ıa, investigaci´on y tecnolog´ıa. 2016;17(2):257-27 Norvig P, Russell S. Inteligencia artificial. Editora Campus. 2004;20. Cong DT, Khoudour L, Achard C, Meurie C, Lezoray O. People re identification by spectral classification of silhouettes. Signal Process. 2010;90(8):2362-2374. Salter T, Werry I, Michaud F. Going into the wild in child–robot interaction studies: Issues in social robotic development. Intelligent Service Robotics. 2008;1(2):93-108. Wechsler H, Phillips JP, Bruce V, Soulie FF, Huang TS. Face recognition: From theory to applications. Vol 163. Springer Science & Business Media; 2012. Zeng J, Zeng J, Qiu X. Deep learning based forensic face verification in videos. . 2017:77-80. Rashid RA, Mahalin NH, Sarijari MA, Aziz AAA. Security system using biometric technology: Design and implementation of voice recognition system (VRS). . 2008:898-902. einrich C, Volkhardt M, Gross H. Appearance-based 3d upper body pose estimation and person re-identification on mobile robots. . 2013:4384-4390. Barea R, Bergasa LM, L´opez E, Escudero MS, Leon C. Face recognition for social interaction with a personal robotic assistant. . 2005;1:382-385. Hsu C, Lin C, Su W, Cheung G. Sigan: Siamese generative adversarial network for identity-preserving face hallucination. IEEE Trans Image Process. 2019;28(12):6225-6236. D’Auria D, Persia F, Bettini F, Helmer S, Siciliano B. Sarri: A smart rapiro robot integrating a framework for automatic high-level surveillance event detection. . 2018:238-241 Matamoros M, Rascon C, Hart J, et al. RoboCup@Home rules & regulations. Kim D, Yoon H, Chi S, Cho Y. Face identification for human robot interaction: Intelligent security system for multi-user working environment on PC. . 2006:617-622. Akbulut Y, S¸eng¨ur A, Budak U, Ekici S. Deep learning based face ¨ liveness detection in videos. . 2017:1-4. Bae H, Lee H, Lee S. Voice recognition based on adaptive MFCC and deep learning. . 2016:1542-1546. Chakraborty K, Talele A, Upadhya S. Voice recognition using MFCC algorithm. International Journal of Innovative Research in Advanced Engineering (IJIRAE). 2014;1(10):2349-2163. Fang X, Gu W, Huang C. A method of skin color identification based on color classification. . 2011;4:2355-2358. Song A, Hu Q, Ding X, Di X, Song Z. Similar face recognition using the IE-CNN model. IEEE Access. 2020;8:45244-45253. Sinith MS, Salim A, Sankar KG, Narayanan KS, Soman V. A novel method for text-independent speaker identification using mfcc and gmm. . 2010:292-296. Po lap D, Wo´zniak M. Voice recognition by neuro-heuristic method. Tsinghua Science and Technology. 2018;24(1):9-17. Christensen HI, Batzinger T, Bekris K, et al. A roadmap for us robotics: From internet to robotics. Computing Community Consortium. 2009;44. Siciliano B, Caccavale F, Zwicker E, et al. Euroc-the challenge initiative for european robotics. . 2014:1-7. Tome P, Fierrez J, Vera-Rodriguez R, Nixon MS. Soft biometrics and their application in person recognition at a distance. IEEE Transactions on information forensics and security. 2014;9(3):464-475. Shan C, Gong S, McOwan PW. Facial expression recognition based on local binary patterns: A comprehensive study. Image Vision Comput. 2009;27(6):803-816. Ephraim T, Himmelman T, Siddiqi K. Real-time viola-jones face detection in a web browser. . 2009:321-328. Su´arez EJC. Tutorial sobre máquinas de vectores soporte (sVM). Tutorial sobre Máquinas de Vectores Soporte (SVM). 2014:1-12 Chandrasekhar AM, Raghuveer K. An effective technique for intrusion detection using neuro -fuzzy and radial svm classifier. In: Computer networks & communications (NetCom). Springer; 2013:499-507 Olabe XB. Redes neuronales artificiales y sus aplicaciones. Publicaciones de la Escuela de Ingenieros. 1998. Gazalba I, Reza NGI. Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. . 2017:294-298. Muda L, Begam M, Elamvazuthi I. Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. arXiv preprint arXiv:1003.4083. 2010. Eskidere O, G¨urhanlı A. Voice disorder classification based on ¨ multitaper mel frequency cepstral coefficients features. Computational and mathematical methods in medicine. 2015;2015. Gupta A, Gupta H. Applications of MFCC and vector quantization in speaker recognition. . 2013:170-173 Huang T, Peng H, Zhang K. Model selection for gaussian mixture models. Statistica Sinica. 2017:147-169. Buza E, Akagic A, Omanovic S. Skin detection based on image color segmentation with histogram and k-means clustering. . 2017:1181-1186. He K, Gkioxari G, Doll´ar P, Girshick R. Mask r-cnn. . 2017:2961-2969. Zhang K, Zhang Z, Li Z, Qiao Y. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett. 2016;23(10):1499-1503. Asafu-Adjei JK, Betensky RA. A pairwise na¨ıve bayes approach to bayesian classification. Int J Pat Recognit Artif Intell. 2015;29(07):1550023. Jang E, Gu S, Poole B. Categorical reparameterization with gumbel softmax. arXiv preprint arXiv:1611.01144. 201 Furui S, Kikuchi T, Shinnaka Y, Hori C. Speech-to-text and speech to-speech summarization of spontaneous speech. IEEE Transactions on Speech and Audio Processing. 2004;12(4):401-408. ROS.org. https://wiki.ros.org/. Rahmad C, Asmara RA, Putra D, Dharma I, Darmono H, Muhiqqin I. Comparison of viola-jones haar cascade classifier and histogram of oriented gradients (HOG) for face detection. . 2020;732(1):012038. Shinwari AR, Jalali Balooch A, Alariki AA, Abduljalil Abdulhak S. A comparative study of face recognition algorithms under facial expression and illumination. ICACT. Feb 2019:390-394. https://ieeexplore.ieee.org/document/8702002. doi: 10.23919/ICACT.2019.8702002. Weng Z, Li L, Guo D. Speaker recognition using weighted dynamic MFCC based on GMM. . 2010:285-288. Shahin I, Nassif AB, Hamsa S. Novel cascaded gaussian mixture model-deep neural network classifier for speaker identification in emotional talking environments. Neural Computing and Applications. 2020;32(7):2575-2587. Kumari TJ, Jayanna HS. Comparison of LPCC and MFCC features and GMM and GMM-UBM modeling for limited data speaker verification. . 2014:1-6. Thakker M, Vyas S, Ved P, Therese SS. Speaker identification in a multi-speaker environment. In: Information and communication technology for sustainable development. Springer; 2018:239-244. Amante R, S, Tingzon I. Speaker recognition using mel frequency cepstral coefficients with spectral subband centroids features. . Reynolds DA, Rose RC. Robust text-independent speaker identification using gaussian mixture speaker models. IEEE transactions on speech and audio processing. 1995;3(1):72-83. Rozario B, Thomas A, Mathew D. Performance comparison of phoneme modeling using MFCC and IHCC features with various optimization algorithms for neural network architectures. . 2019:240- 245 Kamruzzaman SM, Karim A, Islam M, Haque M. Speaker identification using mfcc-domain support vector machine. arXiv preprint arXiv:1009.4972. 2010. Mansour A, Lachiri Z. SVM based emotional speaker recognition using MFCC-SDC features. International Journal of Advanced Computer Science and Applications. 2017;8(4):538-544. Di Nuovo A, Conti D, Trubia G, Buono S, Di Nuovo S. Deep learning systems for estimating visual attention in robot-assisted therapy of children with autism and intellectual disability. Robotics. 2018;7(2):25. Clearpath Robotics. Husky UGV - outdoor field research robot by clearpath. https://clearpathrobotics.com/husky-unmanned ground vehicle-robot/. ROS Wiki. rviz. http://wiki.ros.org/rviz |
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Mateus Rojas, ArmandoPlazas Pirabán, Lina AlejandraBetancur Sanchez, Bryan Stevenhttps://orcid.org/0000-0002-2399-4859https://scholar.google.com/citations?user=1az5o_IAAAAJ&hl=eshttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000680630Universidad Santo Tomás2021-09-17T15:04:03Z2021-09-17T15:04:03Z2021-09-16Plazas Pirabán, L.A. & Betancur Sanchez, B.S. (2021) Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiar [Trabajo de grado pregrado Ingeniería Electrónica] Repositorio Institucionalhttp://hdl.handle.net/11634/35563reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEste documento presenta el desarrollo de un algoritmo de re-identificación multimodal para mejorar la interacción Humano-Robot en el ámbito de asistencia doméstica. De esta manera, se integraron diferentes estrategias de reconocimiento de personas como lo son reconocimiento facial, por voz y por características soft-biométricas (color de cabello, ojos y piel). Para esto, en primer lugar se realizó una consulta bibliográfica donde se eligieron posibles algoritmos a utilizar; luego se implementaron y se realizaron diferentes pruebas con el fin de elegir los algoritmos que presentaban mejores resultados por cada estrategia de re-identificación, después se integraron en un único desarrollo basado en regresión lineal múltiple el cual tuvo un porcentaje de acierto del 97.4%. De igual manera, se implementó todo el sistema en ROS (sistema operativo robótico) y se realizaron pruebas donde se evaluó si el algoritmo reconocía órdenes básicas personalizadas.This document presents the development of a multimodal re-identification algorithm to improve Human-Robot interaction in the home care setting. In this way, different people recognition strategies were integrated, such as facial recognition, voice recognition and soft-biometric characteristics (hair, eye and skin color). For this, in the first place a bibliographic consultation was carried out where possible algorithms to be used were chosen; Later, different tests were implemented and carried out in order to choose the algorithms that presented the best results for each re-identification strategy, then they were integrated into a single development based on multiple linear regression, which had a 97.4% success rate. Similarly, the entire system was implemented in ROS (robotic operating system) and tests were carried out where it was evaluated if the algorithm recognized personalized basic orders.Ingeniero Electronicohttp://unidadinvestigacion.usta.edu.coPregradoapplication/pdfspaUniversidad Santo TomásPregrado Ingeniería ElectrónicaFacultad de Ingeniería ElectrónicaAtribución-NoComercial-SinDerivadas 2.5 ColombiaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Desarrollo de un algoritmo de re-identificación multi-modal de personas para mejorar la asistencia personalizada en una casa familiarFace recognitionVoice recognitionSoft-biometric characteristicsInteligencia artificialDeep learningAlgoritmo multimodalReconocimiento rostroReconocimiento vozCaracterísticas soft-biométricasTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionFormación de Recurso Humano para la Ctel: Trabajo de grado de Pregradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA BogotáAlpaydin E. Introduction to machine learning. MIT press; 2020.Kapusta A, Erickson Z, Clever HM, et al. Personalized collaborative plans for robot-assisted dressing via optimization and simulation. Autonomous Robots. 2019;43(8):2183-2207.Zhang DD. Automated biometrics: Technologies and systems. Vol 7. Springer Science & Business Media; 2013.Alejandra MD. Re-identificaci´on de personas a trav´es de sus caracter´ısticas soft-biom´etricas en un entorno multi-c´amara de video vigilancia. Ingenier´ıa, investigaci´on y tecnolog´ıa. 2016;17(2):257-27Norvig P, Russell S. Inteligencia artificial. Editora Campus. 2004;20.Cong DT, Khoudour L, Achard C, Meurie C, Lezoray O. People re identification by spectral classification of silhouettes. Signal Process. 2010;90(8):2362-2374.Salter T, Werry I, Michaud F. Going into the wild in child–robot interaction studies: Issues in social robotic development. Intelligent Service Robotics. 2008;1(2):93-108.Wechsler H, Phillips JP, Bruce V, Soulie FF, Huang TS. Face recognition: From theory to applications. Vol 163. Springer Science & Business Media; 2012.Zeng J, Zeng J, Qiu X. Deep learning based forensic face verification in videos. . 2017:77-80.Rashid RA, Mahalin NH, Sarijari MA, Aziz AAA. Security system using biometric technology: Design and implementation of voice recognition system (VRS). . 2008:898-902.einrich C, Volkhardt M, Gross H. Appearance-based 3d upper body pose estimation and person re-identification on mobile robots. . 2013:4384-4390.Barea R, Bergasa LM, L´opez E, Escudero MS, Leon C. Face recognition for social interaction with a personal robotic assistant. . 2005;1:382-385.Hsu C, Lin C, Su W, Cheung G. Sigan: Siamese generative adversarial network for identity-preserving face hallucination. IEEE Trans Image Process. 2019;28(12):6225-6236.D’Auria D, Persia F, Bettini F, Helmer S, Siciliano B. Sarri: A smart rapiro robot integrating a framework for automatic high-level surveillance event detection. . 2018:238-241Matamoros M, Rascon C, Hart J, et al. RoboCup@Home rules & regulations.Kim D, Yoon H, Chi S, Cho Y. Face identification for human robot interaction: Intelligent security system for multi-user working environment on PC. . 2006:617-622.Akbulut Y, S¸eng¨ur A, Budak U, Ekici S. Deep learning based face ¨ liveness detection in videos. . 2017:1-4.Bae H, Lee H, Lee S. Voice recognition based on adaptive MFCC and deep learning. . 2016:1542-1546.Chakraborty K, Talele A, Upadhya S. Voice recognition using MFCC algorithm. International Journal of Innovative Research in Advanced Engineering (IJIRAE). 2014;1(10):2349-2163.Fang X, Gu W, Huang C. A method of skin color identification based on color classification. . 2011;4:2355-2358.Song A, Hu Q, Ding X, Di X, Song Z. Similar face recognition using the IE-CNN model. IEEE Access. 2020;8:45244-45253.Sinith MS, Salim A, Sankar KG, Narayanan KS, Soman V. A novel method for text-independent speaker identification using mfcc and gmm. . 2010:292-296.Po lap D, Wo´zniak M. Voice recognition by neuro-heuristic method. Tsinghua Science and Technology. 2018;24(1):9-17.Christensen HI, Batzinger T, Bekris K, et al. A roadmap for us robotics: From internet to robotics. Computing Community Consortium. 2009;44.Siciliano B, Caccavale F, Zwicker E, et al. Euroc-the challenge initiative for european robotics. . 2014:1-7.Tome P, Fierrez J, Vera-Rodriguez R, Nixon MS. Soft biometrics and their application in person recognition at a distance. IEEE Transactions on information forensics and security. 2014;9(3):464-475.Shan C, Gong S, McOwan PW. Facial expression recognition based on local binary patterns: A comprehensive study. Image Vision Comput. 2009;27(6):803-816.Ephraim T, Himmelman T, Siddiqi K. Real-time viola-jones face detection in a web browser. . 2009:321-328.Su´arez EJC. Tutorial sobre máquinas de vectores soporte (sVM). Tutorial sobre Máquinas de Vectores Soporte (SVM). 2014:1-12Chandrasekhar AM, Raghuveer K. An effective technique for intrusion detection using neuro -fuzzy and radial svm classifier. In: Computer networks & communications (NetCom). Springer; 2013:499-507Olabe XB. Redes neuronales artificiales y sus aplicaciones. Publicaciones de la Escuela de Ingenieros. 1998.Gazalba I, Reza NGI. Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. . 2017:294-298.Muda L, Begam M, Elamvazuthi I. Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. arXiv preprint arXiv:1003.4083. 2010.Eskidere O, G¨urhanlı A. Voice disorder classification based on ¨ multitaper mel frequency cepstral coefficients features. 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