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Towards explaining deep learning networks to distinguish facial expressions of pain and emotions

  • Deep learning networks are successfully used for object and face recognition in images and videos. In order to be able to apply such networks in practice, for example in hospitals as a pain recognition tool, the current procedures are only suitable to a limited extent. The advantage of deep learning methods is that they can learn complex non-linear relationships between raw data and target classes without limiting themselves to a set of hand-crafted features provided by humans. However, the disadvantage is that due to the complexity of these networks, it is not possible to interpret the knowledge that is stored inside the network. It is a black-box learning procedure. Explainable Artificial Intelligence (AI) approaches mitigate this problem by extracting explanations for decisions and representing them in a human-interpretable form. The aim of this paper is to investigate the explainable AI method Layer-wise Relevance Propagation (LRP) and apply it to explain how a deep learning network distinguishes facial expressions of pain from facial expressions of emotions such as happiness and disgust.

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Document Type:Conference Object
Author:Katharina Weitz, Teena Hassan, Ute Schmid, Jens Garbas
Parent Title (English):Längle, Puente León et al. (Hg.): Forum Bildverarbeitung 2018, 29.-30. Novemver 2018 in Karlsruhe
Number of pages:12
First Page:197
Last Page:208
Publisher:KIT Scientific Publishing
Place of publication:Karlsruhe
Date of first publication:2018/11/21
Keyword:Explainable artificial intelligence; deep learning; emotion recognition; pain recognition
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2023/04/13