.png){kind=icon}
Return
Spatio-temporal deep learning for improved face presentation attack detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2025.113059.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Face presentation attack detection (FacePAD) is critical for securing face recognition systems against attacks such as printed photos, videos, and 3D masks. Existing methods often struggle with generalizability, computational efficiency, and handling sophisticated attacks, particularly in resource-constrained environments. To address these challenges, this study proposes a lightweight CNN-based architecture, MobileNetV3, integrated with spatio-temporal feature extraction. The proposed method effectively captures both dynamic and static characteristics and achieves state-of-the-art performance, including an Equal Error Rate (EER) of 0.0% on the Replay-Attack and Replay-Mobile datasets, and 0.83% on the challenging ROSE-Youtu dataset. With real-time efficiency, processing 256 samples in 11 ms, the model is suitable for deployment on mobile and embedded platforms. This work demonstrates that lightweight architectures with spatio-temporal features can balance computational efficiency and accuracy, setting a benchmark for practical FacePAD systems in applications like mobile authentication, surveillance, and access control, enhancing biometric security.
Keywords:
Face presentation attack detection
Face anti-spoofing
MobileNetV3
Spatio-temporal
Deep learning
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveA Novel Deep Learning Architecture With Image Diffusion for Robust Face Presentation Attack Detection.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveRemote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection
PREAI
ShareSave
ShareSaveFully supervised contrastive learning in latent space for face presentation attack detectionAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
ShareSaveShareSave
View more