\section{Experiment Results}

We constructed a test set using an unseen dataset to evaluate its generalization performance. Specifically, we use a Canadian ID dataset collected from a real-world scenario.
\subsection{ID clustering and annotation}
Manual ID labeling is labor-intensive, time-consuming, and difficult to scale as new document layouts are continuously introduced. Since ID embeddings typically exhibit high intra-class variance and low inter-class variance, we leverage clustering to group a large collection of IDs in the embedding space, enabling bulk pseudo-labeling with minimal manual intervention.

We apply k-means clustering to the learned ID embeddings and visualize the resulting distribution using 2D t-SNE (like that shown in Figure \ref{fig:fig1} but are different datasets). Major layouts naturally form well-separated clusters, indicating that the embeddings capture discriminative layout-level features. Clustering provides a strong initialization for large-scale annotation, significantly reducing manual effort to reviewing and correcting misclustered samples.

\subsection{Model performance on test set}
The supervised training minimizes intra-class distance while maximizing inter-class distance, hence, are these two metrics are examined in the ablation study. To evaluate the clustering quality, we employ the Silhouette score defined as
\begin{equation}
s(i)=\frac{b(i)-a(i)}{max(a(i),b(i))}
\end{equation}
where $a(i)$ is the average distance between sample $i$ and all other samples in the same class (intra-class distance), and  $b(i)$ is the minimum average distance between sample $i$ and all samples in nearest neighbouring class (the next best alternative). We further employ the Davies--Bouldin index (DBI) to quantify the clustering validity, which is defined as 
\begin{equation}
\mathrm{DBI} = \frac{1}{K} \sum_{i=1}^{K} \max_{j \neq i}
\left( \frac{S_i + S_j}{M_{ij}} \right),
\end{equation}
where $K$ is the number of clusters, $S_i$ denotes the within-class scatter of cluster $i$, and $M_{ij}$ is the distance between the centroids of class $i$ and $j$.

Table \ref{tab:table1} exhibits the clustering metrics on the Canadian ID test set. Comparing the original and the fine-tuned domain-specific DINOv3 models, the fine-tuned model achieves a higher Silhouette score (0.132 vs. 0.123) and a lower DBI (2.501 vs. 2.718), indicating superior cluster compactness and separation on unseen ID layouts. Given this improved embedding quality, we adopt the fine-tuned model for subsequent supervised discriminative training.

On supervised learning, we evaluate four configurations with different loss formulations: (1) ArcFace loss only, (2) SupCon loss only, (3) ArcFace + SupCon, and (4) ArcFace + SupCon + Center loss. The corresponding weighting coefficients ($\lambda_A$, $\lambda_S$, and $\lambda_C$) are listed in Table \ref{tab:table1}. We use an equal weighting for ArcFace and SupCon losses since both are log-softmax on cosine similarities. For the center loss, we adopt a $\lambda_C = 0.003$, which is originally used by Wen et al.~\cite{wen2016discriminativefeaturelearningapproach}. Experiments show that the combination of all three losses yields the highest Silhouette score and the lowest DBI, demonstrating improved intra-class compactness and inter-class separability. This configuration constitutes our best-performing model.

To assess whether self-supervised fine-tuning is necessary, we conduct an ablation study using the original DINOv3-Large model as the base model. We then apply supervised fine-tuning with the same loss configurations, where the best result is again obtained using the combination of all three losses (for brevity, only this best variant is shown in Table \ref{tab:table1}). Although this model achieves a marginally smaller intra-class distance (0.444 vs. 0.445), its Silhouette score and DBI are inferior to those of the domain-fine-tuned counterpart. This suggests that self-supervised fine-tuning on real-world ID images enables the model to capture richer layout-specific contextual and spatial representations, providing a stronger initialization than the original DINOv3 model.

The ID datasets used for training and the ablation studies described above are proprietary. To facilitate evaluation on a public benchmark, we further assess our approach on the 2025 DeepID Challenge dataset \cite{Korshunov_2025_ICCV}, which contains approximately 4,000 FantasyID images with diverse layouts captured using different devices \cite{vidit2025fantasyiddatasetdetecting}. We evaluate our model on the bona fide subset, and the results are summarized in Table \ref{tab:table2}.

Interestingly, the fine-tuned domain-specific DINOv3 achieves the best Silhouette (0.693) and DBI (0.431) on this benchmark. However, we emphasize that the FantasyID dataset does not fully reflect real-world ID verification conditions. This difference is reflected in the clustering statistics: the Silhouette score on FantasyID (0.693) is substantially higher than that on the real-world ID dataset (0.311), indicating a much cleaner and less noisy distribution. Therefore, while FantasyID serves as a useful public benchmark, its relative homogeneity may not capture the full complexity and distributional challenges encountered in practical deployment settings.

\begin{table*}[t]
    \centering
    \caption{The mean intra-class distance, mean inter-class distance, Silhouette score, and DBI of the test set Canadian IDs, calculated for the DINOv3, the fine-tuned DINOv3, and supervised learning models.}
    \label{tab:table1}
    \renewcommand{\arraystretch}{1.3}
    \begin{tabular}{lcccc}
        \toprule
        \textbf{Model}
            & \textbf{Intra-class} $\downarrow$ 
            & \textbf{Inter-class} $\uparrow$ 
            & \textbf{Silhouette} $\uparrow$ 
            & \textbf{DBI} $\downarrow$ \\
        \midrule

        \textbf{DINOv3}         & 0.446 & 0.655 & 0.123 & 2.718 \\

        \quad $\lambda_A{=}1, \lambda_S{=}1, \lambda_C{=}0.003 $
            & 0.444 & 1.163 & 0.303 & 1.403 \\
        \midrule
        \textbf{Fine-tuned DINOv3}    & \textbf{0.377} & 0.612 & 0.132 & 2.501 \\


        \quad$\lambda_A{=}1, \lambda_S{=}0, \lambda_C{=}0$
        & 0.493 & 1.124 & 0.289 & 1.354 \\

        \quad$\lambda_A{=}0, \lambda_S{=}1, \lambda_C{=}0$
        & 0.669 & 1.179 & 0.180 & 2.033 \\

        \quad$\lambda_A{=}1, \lambda_S{=}1, \lambda_C{=}0$
        & 0.502 & \textbf{1.219} & 0.282 & 1.355 \\

        \quad$\lambda_A{=}1, \lambda_S{=}1, \lambda_C{=}0.003$
        & 0.445 & 1.168 & \textbf{0.311} & \textbf{1.265} \\
        \bottomrule
    \end{tabular}
\end{table*}



\begin{table*}[t]
    \centering
    \caption{The intra-class distance, inter-class distance, Silhouette score, and DBI of the FantasyID dataset.}
    \label{tab:table2}
    \renewcommand{\arraystretch}{1.3}
    \begin{tabular}{lcccc}
        \toprule
        \textbf{Model}
            & \textbf{Intra-class} $\downarrow$ 
            & \textbf{Inter-class} $\uparrow$ 
            & \textbf{Silhouette} $\uparrow$ 
            & \textbf{DBI} $\downarrow$ \\
        \midrule

        \textbf{DINOv3}         & 0.349 & 0.676 & 0.386 & 1.062 \\

        \quad $\lambda_A{=}1, \lambda_S{=}1, \lambda_C{=}0.003$
            & 0.286 & \textbf{1.091} & \textbf{0.693} & \textbf{0.431} \\
        \midrule
        \textbf{Fine-tuned DINOv3}    & 0.290 & 0.643 & 0.410 & 0.988 \\

        \quad$\lambda_A{=}1, \lambda_S{=}0, \lambda_C{=}0$
            & 0.312 & 1.120 & 0.669 & 0.490 \\

        \quad$\lambda_A{=}0, \lambda_S{=}1, \lambda_C{=}0$
            & 0.309 & 0.984 & 0.588 & 0.690 \\

        \quad$\lambda_A{=}1, \lambda_S{=}1, \lambda_C{=}0$
            & 0.286 & 1.031 & 0.667 & 0.537 \\

        \quad$\lambda_A{=}1, \lambda_S{=}1, \lambda_C{=}0.003$
            & \textbf{0.269}  & 1.063 & 0.688 & 0.473 \\
        \bottomrule
    \end{tabular}
\end{table*}




\subsection{ID layout classification}
To evaluate the discriminative power of the learned embeddings for downstream tasks, we perform ID layout classification for the Canadian ID dataset that contains 19 primary layouts (rarely-seen layouts are excluded for reliable evaluation). We append a lightweight multilayer perceptron (MLP) head to the embedding, consisting of a 256-unit hidden layer, a ReLU activation and a dropout layer with a rate of 0.2. We adopt an 80/20 split within each layout category to ensure balanced supervision across classes. The model is trained using cross-entropy loss. On the held-out test set, the classifier achieves 99.83\% overall accuracy, demonstrating strong generalization across diverse layouts. In contrast, the original DINOv3 model achieves 99.32\% overall accuracy, indicating that the supervised fine-tuning significantly improves the model's ability to generalize to unseen layouts.

Although the current experiment focuses on a moderate-scale layout taxonomy, real-world global ID systems may involve hundreds or thousands of distinct layouts. Scaling layout classification to such a large and dynamically evolving label space introduces additional challenges, including long-tail distribution effects and fine-grained inter-layout similarity. We leave comprehensive evaluation on large-scale global ID datasets as future work.