  A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a
discriminative multi-scale facial representations for face recognition and
verification. In FPVT, Face Spatial Reduction Attention (FSRA) and
Dimensionality Reduction (FDR) layers are employed to make the feature maps
compact, thus reducing the computations. An Improved Patch Embedding (IPE)
algorithm is proposed to exploit the benefits of CNNs in ViTs (e.g., shared
weights, local context, and receptive fields) to model lower-level edges to
higher-level semantic primitives. Within FPVT framework, a Convolutional
Feed-Forward Network (CFFN) is proposed that extracts locality information to
learn low level facial information. The proposed FPVT is evaluated on seven
benchmark datasets and compared with ten existing state-of-the-art methods,
including CNNs, pure ViTs, and Convolutional ViTs. Despite fewer parameters,
FPVT has demonstrated excellent performance over the compared methods. Project
page is available at https://khawar-islam.github.io/fpvt/
