ArticleSensors (Basel, Switzerland)2019
A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Embedded Vision System for Thermal Face Detection Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Flow-Field Inference for Turbulent Exhale Flow Measurement.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review.Sensors (Basel, Switzerland) · 2023Review
- A deep learning model for classifying human facial expressions from infrared thermal images.Scientific reports · 2021Article
- Thermal Infrared Face Recognition.Cureus · 2021Review
- Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.