Understanding presumption system from facial images

Atsushi Mimura, Masafumi Hagiwara

Research output: Contribution to conferencePaperpeer-review


In this paper, we propose an understanding presumption system from facial images using a three-layered neural network. It can presume a degree of understanding from facial expressions; it can recognize whether a person understands a question or not. Feature points are located on each facial image and are used to extract an expression information. The expression information is given as an input and the system presumes the degree of understanding based on the facial images into 5 levels, from NOT UNDERSTAND to WELL UNDERSTAND. The network is learned using Back Propagation algorithm. The average presumption rates of the proposed system was 71.3%.

Original languageEnglish
Number of pages6
Publication statusPublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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