The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking

The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking

In this paper, we explore the effect of using different convolutional layers, batch normalization and the global average pooling layer upon a convolutional neural network (CNN) based gaze tracking system. A novel method is proposed to label the participant’s face images as gaze points retrieved from...

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Bibliographic Details
Main Authors: Hui-Hui Chen, Bor-Jiunn Hwang, Jung-Shyr Wu, Po-Ting Liu
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Algorithms
Subjects:
gaze tracking
convolution neural network
batch normalization
global average pooling layer
Online Access:https://www.mdpi.com/1999-4893/13/5/127

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