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

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

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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 crafthouse by fortessa glass smoking box with handheld smoker 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 allflex 840 tags stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.