Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches

Kopaczka, Marcin; Nestler, Jan; Merhof, Dorit

doi:10.1007/978-3-319-70353-4_44

Cited by 35 publications

(36 citation statements)

References 19 publications

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“…As shown in [4], learning-based approaches outperform algorithm-based methods in terms of precision and especially robustness. Since we want our system to be usable in real-world conditions, only learning-based methods are an option.…”

Section: Methodsmentioning

confidence: 99%

“…Since we want our system to be usable in real-world conditions, only learning-based methods are an option. For thermal face detection, we trained a HOG-SVM based face detector which, according to [4], gives a good balance between runtime, robustness, precision and implementation complexity.…”

Section: Methodsmentioning

confidence: 99%

“…This issue is addressed in current face detection algorithms for the visual spectrum by using machine learning techniques to reliably classify image parts containing faces with little or no restrictions towards the remaining image content. Recently, a number of authors have analyzed how these methods can be applied to thermal images as well [4,5,6]. In their research, these authors were able to show that face detectors based on machine learning constantly outperform specialized approaches if a suitable training database is provided.…”

Section: State Of the Artmentioning

confidence: 99%

“…At the same time, this is an extremly difficult task in the visual domain. Several methods for respiratory signal extraction and analysis have been published, examples include [12] and [4]. In [22], the authors additionally used a 3D depth camera to improve tracking and detection robustness.…”

Section: State Of the Artmentioning

confidence: 99%

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A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

Kopaczka

Breuer

Schock

et al. 2019

Sensors

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

Kopaczka

Breuer

Schock

et al. 2019

Sensors

Self Cite

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“…However, these benchmark methods failed to detect landmarks correctly when applied to the IR images (Figure 3a and 3b). Previous work on infrared based facial analysis and ROI tracking primarily explored the use of standard machine learning techniques (Wesley, Buddharaju et al 2012, Ghiass, Arandjelović et al 2014, Kopaczka, Acar et al 2016, Kopaczka, Nestler et al 2017. These models allow optimal landmark detection in some cases but need further improvement as they rely on data attributes (features) which in the case of IR facial images lack the details present in visible spectrum images.…”

Section: Facial Landmark Estimationmentioning

confidence: 99%

Body and brain responses underlying salient emotional information processing

Sonkusare¹

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Cognitive neuroscience has traditionally focused on simple tasks, presented sparsely and with abstract stimuli. While this approach has yielded fundamental insights into functional specialization in the brain, its ecological validity remains uncertain: Do these tasks fully capture how our brains function in the real world, where stimuli are dynamic, multimodal and crowded? Ecologically valid paradigms that approximate real life scenarios, using stimuli such as films, spoken narratives, music and multiperson games emerged in response to these concerns over a decade ago. We critically appraise whether this approach has delivered on its promise to deliver new insights into brain function. We highlight the challenges, technological innovations and clinical opportunities that are required should this field meet its full potential.

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