Paper Info
Title
Visualization Method Corresponding to Regression Problems and Its Application to Deep Learning-Based Gaze Estimation Model.
Abstract
The human gaze contains substantial personal information and can be extensively employed in several applications if its relevant factors can be accurately measured. Further, several fields could be substantially innovated if the gaze could be analyzed using popular and familiar smart devices. Deep learning-based methods are robust, making them crucial for gaze estimation on smart devices. However, because internal functions in deep learning are black boxes, deep learning systems often make estimations for unclear reasons. In this paper, we propose a visualization method corresponding to a regression problem to solve the black box problem of the deep learning-based gaze estimation model. The proposed visualization method can clarify which region of an image contributes to deep learning-based gaze estimation. We visualized the gaze estimationmodel proposed by a research group at the Massachusetts Institute of Technology. The accuracy of the estimation was low, even when the facial features important for gaze estimation were recognized correctly. The effectiveness of the proposed method was further determined through quantitative evaluation using the area over the MoRF perturbation curve (AOPC).
Year
DOI
Venue
2020
10.20965/jaciii.2020.p0676
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Keywords
DocType
Volume
CNN,eye tracking,Grad-CAM,regression problem
Journal
24
Issue
ISSN
Citations 
5
1343-0130
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Daigo Kanda100.34
Shin Kawai200.34
Hajime Nobuhara319234.02