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A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings †
We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806182/ https://www.ncbi.nlm.nih.gov/pubmed/31554260 http://dx.doi.org/10.3390/s19194135 |
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author | Kopaczka, Marcin Breuer, Lukas Schock, Justus Merhof, Dorit |
author_facet | Kopaczka, Marcin Breuer, Lukas Schock, Justus Merhof, Dorit |
author_sort | Kopaczka, Marcin |
collection | PubMed |
description | We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions. |
format | Online Article Text |
id | pubmed-6806182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68061822019-11-07 A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † Kopaczka, Marcin Breuer, Lukas Schock, Justus Merhof, Dorit Sensors (Basel) Article We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions. MDPI 2019-09-24 /pmc/articles/PMC6806182/ /pubmed/31554260 http://dx.doi.org/10.3390/s19194135 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kopaczka, Marcin Breuer, Lukas Schock, Justus Merhof, Dorit A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † |
title | A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † |
title_full | A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † |
title_fullStr | A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † |
title_full_unstemmed | A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † |
title_short | A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings † |
title_sort | modular system for detection, tracking and analysis of human faces in thermal infrared recordings † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806182/ https://www.ncbi.nlm.nih.gov/pubmed/31554260 http://dx.doi.org/10.3390/s19194135 |
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