Authors:
Tristan Cladière
;
Olivier Alata
;
Christophe Ducottet
;
Hubert Konik
and
Anne-Claire Legrand
Affiliation:
Université Jean Monnet Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France
Keyword(s):
Emotion Recognition, People Detection, Context, Amalgamation, Multiple Teachers.
Abstract:
Fine-grained emotion recognition using the whole context inside images is a challenging task. Usually, the approaches to solve this problem analyze the scene from different aspects, for example people, place, object or interactions, and make a final prediction that takes all this information into account. Despite giving promising results, this requires specialized pre-trained models, and multiple pre-processing steps, which inevitably results in long and complex frameworks. To obtain a more practicable solution that would work in real time scenario with limited resources, we propose a method inspired by the amalgamation process to incorporate specialized knowledge from multiple teachers inside a student composed of a single architecture. Moreover, the student is not only capable of treating all subjects simultaneously by creating emotion maps, but also to detect the subjects in a bottom-up manner. We also compare our approach with the traditional method of fine-tuning pre-trained mod
els, and show its superiority on two databases used in the context-aware emotion recognition field.
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