MFFC-Net: multi-scale feature fusion-based coordination network for gaze estimation

Zichen Zhao; Weitao Ke; Qingsong Yan; Xiaofeng Lu

doi:10.1117/12.2681656

1 June 2023 MFFC-Net: multi-scale feature fusion-based coordination network for gaze estimation

Zichen Zhao, Weitao Ke, Qingsong Yan, Xiaofeng Lu

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127182A (2023) https://doi.org/10.1117/12.2681656
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

Appearance-based methods with deep learning can predict the point of gaze by using a monocular camera, which requires a large sample to learn. However, existing appearance-based gaze estimation methods with deep learning mainly use face and eye images or only use a single face image, ignoring the correlation between face features and eye features In response to this issue, we propose a coordination model where face feature extraction is the gaze estimation network and eye feature extraction is the coordination network, which deeply fuses the eye-face feature relationships to perform the gaze estimation task. The model achieves good results on MPIIFaceGaze dataset and GazeCapture dataset.

Citation Download Citation

Zichen Zhao, Weitao Ke, Qingsong Yan, and Xiaofeng Lu "MFFC-Net: multi-scale feature fusion-based coordination network for gaze estimation", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127182A (1 June 2023); https://doi.org/10.1117/12.2681656

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