Open AccessHead pose‐free gaze estimation using domain adaptation
Author(s) -
Ahn Byungtae,
Seo Minseok,
Choi DongGeol
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12247
Subject(s) - artificial intelligence , gaze , computer science , computer vision , convolutional neural network , grayscale , pose , adaptation (eye) , feature (linguistics) , head (geology) , domain (mathematical analysis) , pattern recognition (psychology) , human head , image (mathematics) , mathematics , mathematical analysis , linguistics , philosophy , physics , geomorphology , acoustics , optics , absorption (acoustics) , geology
Human gaze information has been widely used in various areas, such as medical diagnosis and human–computer interactions (HCI). This study proposes a head pose‐free 3D gaze estimation method using a deep convolutional neural network (DCNN). To infer gaze direction, only a small grayscale image is required without any special devices such as an infrared (IR) illuminator and RGBD sensor. A domain adaptation method to reduce the feature gap between real and synthetic image data is also proposed here. Moreover, a novel synthetic dataset (SynFace) that contains head poses, gaze directions, and facial landmarks is established and released. The proposed method outperforms state‐of‐the‐art methods and achieves a mean error of less than 4 ○ .