ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection

no code implementations • 3 Mar 2022 • Zuheng Ming, Zitong Yu, Musab Al-Ghadi, Muriel Visani, Muhammad MuzzamilLuqman, Jean-Christophe Burie

Instead of using coarse image patches with single-scale as in ViT, we propose the Multi-scale Multi-Head Self-Attention (MsMHSA) architecture to accommodate multi-scale patch partitions of Q, K, V feature maps to the heads of transformer in a coarse-to-fine manner, which enables to learn a fine-grained representation to perform pixel-level discrimination for face PAD.

Binary Classification Face Presentation Attack Detection