Wearing head-mounted display (HMD) makes previous research regarding emotion recognition using machine vision ineffective since they utilized entire face images for training. In this paper, we trained the convolutional neural networks (CNNs) which are capable of estimating the emotions from the images of a face wearing a HMD by hiding eyes and eyebrows from existing face-emotion dataset. Our analysis based on the class activation maps show that it is capable of classifying emotions without the eyes and the eyebrows which ar to serve useful information in recognizing emotions. This implies the possibility of estimating the emotions from the images of humans wearing HMDs using machine vision.
|Title of host publication||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||2|
|Publication status||Published - 2019 Mar|
|Event||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Osaka, Japan|
Duration: 2019 Mar 23 → 2019 Mar 27
|Name||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings|
|Conference||26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019|
|Period||19/3/23 → 19/3/27|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea govenment (MSIT) under Grant 2018R1A4A1025986. Additionally, this work was supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institue for the Advancement of Technology (KIAT) (N0002385, 2017).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Media Technology