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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Kensei Tsuchida
Toyo University
Computer Science & IT Electrical & Electronics Engineering

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Interactive Visualization System for Psychological Topology Yasunori Shiono; Chieko Kato; Koichiro Aoki; Kensei Tsuchida
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 4: December 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v7i4.1534

Abstract

Recently, there is increasing interest in mental support activities, including mental health care, counseling, and mental training in workplaces, schools, and sports teams. As a background to these things, various analysis methods have been developed to clarify and visualize the subject’s mental state based on these data. We tried to reveal and visualize the transition patterns of the subjects’ mental states by analyzing their utterances. Furthermore, we developed an interactive system of visualization of psychological state to support visual understanding of psychological topology. Features have been implemented to enable multidimensional data to visualize the movements shown on the SOM map. In this paper, we describe the system that can interactively visualize psychological states.
An optimization of facial feature point detection program by using several types of convolutional neural network Shyota Shindo; Takaaki Goto; Tadaaki Kirishima; Kensei Tsuchida
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp827-834

Abstract

Detection of facial feature points is an important technique used for biometric authentication and facial expression estimation. A facial feature point is a local point indicating both ends of the eye, holes of the nose, and end points of the mouth in the face image. Many researches on face feature point detection have been done so far, but the accuracy of facial organ point detection is improving by the approach usingConvolutional Neural Network (CNN). However, CNN not only takes time to learn but also the neural network becomes a complicated model, so it is necessary to improve learning time and detection accuracy. In this research, the improvement of the detection accuracy of the learning speed is improved by increasing the convolution layer.
Co-Authors Chieko Kato Koichiro Aoki Shyota Shindo Tadaaki Kirishima Takaaki Goto Yasunori Shiono