Face anti-spoofing recognition is a hot and challenging research topic that has received much attention from the computer vision and pattern recognition communities in the past. Owing to the development of deep learning and big data, recent advances of the related research have gained a lot. However, it is still challenging to aim at unknown spoofing attacks, cross-domain generalizations and multi-modal fusions in images and video sequences. This special issue focuses on face anti-spoofing tasks that might benefit from novel methods such as Generative Adversarial Networks (GANs) or AutoML.
We invite paper submissions for the special issue on face anti-spoofing to be published in IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM). We welcome original research papers making theoretical and practical substantial contributions on face anti-spoofing in connection to other computer vision and machine learning topics, including, but not limited to:
- Novel methodologies on face anti-spoofing detection in visual information systems.
- Studies on novel attacks to biometric systems, and solutions
- Meta learning for face anti-spoofing attack
- zero-shot learning for face anti-spoofing attack
- Deep learning methods for biometric authentication systems using visual information
- Novel datasets and evaluation protocols on spoofing prevention on visual and multimodal biometric systems
- Methods for deception detection from visual and multimodal information
- Face antispoof attacks dataset (3D face Mask, multimodal).
- Deep analysis reviews on face anti-spoofing attacks
- Generative models (e.g. GAN) for spoofing attacks
- AutoML for face presentation attack detection
Paper submission and review:
Authors are required to submit contributions online through the TBIOM site at,
https://mc.manuscriptcentral.com/tbiom selecting the choice “FacePAD” that indicates this special issue. Peer reviewing will follow the standard rigorous TBIOM review process. Full length manuscripts are expected to follow TBIOM guidelines.
Paper submission deadline: December, 18th, 2020
First review decision: February, 6th, 2021
Revision deadline: March, 20th, 2021
Final decision: May, 20st, 2021
Jun Wan, Institute of Automation, Chinese Academy of Sciences (CASIA), China, email@example.com
Sergio Escalera, Universitat de Barcelona and Computer Vision Center, Spain, firstname.lastname@example.org
Hugo Jair Escalante, INAOE, Mexico and ChaLearn, Berkeley, California, email@example.com
Guodong Guo, Institute of Deep Learning, Baidu Research, China, firstname.lastname@example.org
Stan Z. Li, Westlake University, China, Stan.ZQ.Li@westlake.edu.cn