A modular system for detection, tracking and analysis of human faces in thermal infrared recordings
Kopaczka M, Breuer L, Schock J, Merhof D (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 19
Article Number: 4135
Journal Issue: 19
DOI: 10.3390/s19194135
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.
Involved external institutions
Germany (DE)How to cite
APA:
Kopaczka, M., Breuer, L., Schock, J., & Merhof, D. (2019). A modular system for detection, tracking and analysis of human faces in thermal infrared recordings. Sensors, 19(19). https://doi.org/10.3390/s19194135
MLA:
Kopaczka, Marcin, et al. "A modular system for detection, tracking and analysis of human faces in thermal infrared recordings." Sensors 19.19 (2019).
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