## 3 Proposed Method
### Weighted Module Based on Contribution Weight  
Through the above experiments, it can be concluded that in the anti-spoofing task, the contributions of the features of different tissue regions inside the fingertip to the anti-spoofing effect show significant differences. In order to quantify this difference, this subsection proposes a Shapley-weighted contribution module (SWCM). By combining  
\begin{table}
\begin{tabular}{c c} \hline
**Image** & **Anti-Spoofing Accuracy** \\ \hline Only the labels of the glass layer & 0.4 \\ \hline Only the labels of the viable epidermis layer & 1 \\ \hline Only the labels of the stratum corneum & 0.05 \\ \hline Glass layer + viable epidermis layer labels & 1 \\ \hline Viable epidermis layer + stratum corneum labels & 1 \\ \hline Glass layer + stratum corneum labels & 0.54 \\ \hline Glass layer + stratum corneum + viable epidermis layer labels & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation study on tissues texture of bona fide and spoof samples.  
the Shapley value with the ablation experiment of the test accuracy of the supervised anti-spoofing task, the final contribution of each tissue structure of the fingertip in the anti-spoofing task can be calculated and converted into a corresponding weight, that is, the weighting coefficient.  
In order to apply the obtained experimental results to optimize the anti-spoofing method, this paper uses three independent U-Net networks for pre-training and fixes the parameters of the segmentation network. U-Net is a convolutional neural network architecture used for semantic segmentation tasks, which was proposed by [PERSON] et al. in 2015 [48]. Its main feature stems from its U-shaped network structure, which has a symmetric encoder and decoder. The middle part is a downsampling path composed of pooling layers and convolutional layers, which is used to extract high-level features of the image. The upsampling module is composed of transposed convolutional layers and skip connections, which are used to restore the feature maps extracted by the encoder to the segmentation results at the original resolution. Through U-Net networks with different parameters, the structural information of each region can be separated. Subsequently, the feature maps of each tissue part obtained by the U-Net segmentation network are multiplied by the previously calculated weighting coefficients, and the outputs of all fingertip tissues are accumulated and finally divided by the number of tissues, so as to obtain the structural and semantic information extracted from the segmentation task. The specific weighting method can be expressed as  
\[X_{k}=F_{en}+\frac{F_{1}w_{1}+F_{2}w_{2}+F_{3}w_{3}}{N_{k}}. \tag{5}\]  
This method uses the Swin Transformer as the backbone network for feature extraction. \(F_{en}\) is the multi-scale feature task map of the image obtained through the Swin Transformer. \(w_{1}\), \(w_{2}\), and \(w_{3}\) are the weights converted from the proportions of the task contribution degrees of different fingertip sample tissues obtained previously. As shown in Figure 10, the obtained multi-scale feature task map and the feature map after fusing the segmentation fusion features are further processed for the fused features through a self-attention residual network. Through the attention mechanism, the dependencies between the fused features can be captured, and the problem of gradient vanishing can be effectively alleviated.  
In this subsection, the key features are amplified according to the contribution ratio, and a contribution-based feature map is obtained accordingly. In this way, the model can focus more on the features with higher contribution, especially those in the dermal region.  
Figure 10: Tissue feature weighted structure based on segmentation network.