2020
DOI: 10.1155/2020/4589260
|View full text |Cite
|
Sign up to set email alerts
|

Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion

Abstract: The performance of the facial landmark detection model will be in trouble when it is under occlusion condition. In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders (GAN-IAs) and deep regression networks. In this model, GAN-IA can restore the occluded face region by utilizing skip concatenation among feature maps to keep more details. Meanwhile, self-atten… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 24 publications
0
2
0
Order By: Relevance
“…Liu et al extended "Xiaoyuan's fish" and independently modeled each character by using cognitive model, vividly and realistically simulating the crowd of people coming and going from the station. The characters in the system have cognitive and behavioral abilities, such as queuing to buy tickets before entering the station, sitting on a chair when tired, queuing to buy drinks when thirsty, and watching street performances [12]. Zhu studied the part design of the role model and its specific implementation technology, combined with the idea of animation design, applied the behavior theory of steering to the group animation design, and designed the group animation design method based on intelligent roles.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Liu et al extended "Xiaoyuan's fish" and independently modeled each character by using cognitive model, vividly and realistically simulating the crowd of people coming and going from the station. The characters in the system have cognitive and behavioral abilities, such as queuing to buy tickets before entering the station, sitting on a chair when tired, queuing to buy drinks when thirsty, and watching street performances [12]. Zhu studied the part design of the role model and its specific implementation technology, combined with the idea of animation design, applied the behavior theory of steering to the group animation design, and designed the group animation design method based on intelligent roles.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In face detection, overfitting is one of the main reasons for the low accuracy of face detection. When the model is overfitted, the classification cannot be carried out when new samples appear because there is more noise in the training set, resulting in insufficient adaptability of the model [ 21 ]. There are many image choices in face detection, and the problem of detection ability will appear when the details or noise are received in the learning of sample data.…”
Section: Methodsmentioning
confidence: 99%