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The Development of Long-Distance Viewing Direction Analysis and Recognition of Observed Objects Using Head Image and Deep Learning

Author

Listed:
  • Yu-Shiuan Tsai

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Nai-Chi Chen

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Yi-Zeng Hsieh

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
    Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202301, Taiwan)

  • Shih-Syun Lin

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan)

Abstract

In this study, we use OpenPose to capture many facial feature nodes, create a data set and label it, and finally bring in the neural network model we created. The purpose is to predict the direction of the person’s line of sight from the face and facial feature nodes and finally add object detection technology to calculate the object that the person is observing. After implementing this method, we found that this method can correctly estimate the human body’s form. Furthermore, if multiple lenses can get more information, the effect will be better than a single lens, evaluating the observed objects more accurately. Furthermore, we found that the head in the image can judge the direction of view. In addition, we found that in the case of the test face tilt, approximately at a tilt angle of 60 degrees, the face nodes can still be captured. Similarly, when the inclination angle is greater than 60 degrees, the facing node cannot be used.

Suggested Citation

  • Yu-Shiuan Tsai & Nai-Chi Chen & Yi-Zeng Hsieh & Shih-Syun Lin, 2021. "The Development of Long-Distance Viewing Direction Analysis and Recognition of Observed Objects Using Head Image and Deep Learning," Mathematics, MDPI, vol. 9(16), pages 1-12, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1880-:d:610213
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