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Algorithm of face anti-spoofing based on pseudo-negative features generation.
Ma, Yukun; Lyu, Chengzhen; Li, Liangliang; Wei, Yajun; Xu, Yaowen.
Afiliación
  • Ma Y; School of Software, Henan Institute of Science and Technology, Xinxiang, China.
  • Lyu C; School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China.
  • Li L; School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
  • Wei Y; School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China.
  • Xu Y; Data and AI Technology Company, China Telecom Corporation Ltd., Beijing, China.
Front Neurosci ; 18: 1362286, 2024.
Article en En | MEDLINE | ID: mdl-38680444
ABSTRACT

Introduction:

Despite advancements in face anti-spoofing technology, attackers continue to pose challenges with their evolving deceptive methods. This is primarily due to the increased complexity of their attacks, coupled with a diversity in presentation modes, acquisition devices, and prosthetic materials. Furthermore, the scarcity of negative sample data exacerbates the situation by causing domain shift issues and impeding robust generalization. Hence, there is a pressing need for more effective cross-domain approaches to bolster the model's capability to generalize across different scenarios.

Methods:

This method improves the effectiveness of face anti-spoofing systems by analyzing pseudo-negative sample features, expanding the training dataset, and boosting cross-domain generalization. By generating pseudo-negative features with a new algorithm and aligning these features with the use of KL divergence loss, we enrich the negative sample dataset, aiding the training of a more robust feature classifier and broadening the range of attacks that the system can defend against.

Results:

Through experiments on four public datasets (MSU-MFSD, OULU-NPU, Replay-Attack, and CASIA-FASD), we assess the model's performance within and across datasets by controlling variables. Our method delivers positive results in multiple experiments, including those conducted on smaller datasets.

Discussion:

Through controlled experiments, we demonstrate the effectiveness of our method. Furthermore, our approach consistently yields favorable results in both intra-dataset and cross-dataset evaluations, thereby highlighting its excellent generalization capabilities. The superior performance on small datasets further underscores our method's remarkable ability to handle unseen data beyond the training set.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Neurosci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Neurosci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza