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Traditional vision transformer consists of two parts: transformer encoder andmulti-layer perception (MLP). The former plays the role of feature learning toobtain better representation, while the latter plays the role ofclassification. Here, the MLP is constituted of two fully connected (FC)layers, average value computing, FC layer and softmax layer. However, due tothe use of average value computing module, some useful information may getlost, which we plan to preserve by the use of alternative framework. In thiswork, we propose a novel vision transformer referred to as adaptive-avg-poolingbased attention vision transformer (AAViT) that uses modules of adaptiveaverage pooling and attention to replace the module of average value computing.We explore the proposed AAViT for the studies on face anti-spoofing usingReplay-Attack database. The experiments show that the AAViT outperforms visiontransformer in face anti-spoofing by producing a reduced equal error rate. Inaddition, we found that the proposed AAViT can perform much better than somecommonly used neural networks such as ResNet and some other known systems onthe Replay-Attack corpus.

Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing