# Multimodal Biometric Template Protection Based on a
## 4 Experiments
### Remarks on Deep-Learning-Based Multimodal Cancelable Biometrics Schemes  
In this section, we present a summary with remarks in Table 7 rather than a comparison between different approaches. This is because a fair comparison between different template protection schemes is very difficult, or even impossible, due to several factors such as the choice of biometric modality, fusion method, datasets, and evaluation metrics.  
### Remarks on Deep-Learning-Based Multimodal Cancelable Biometrics Schemes  
In this section, we present a summary with remarks in Table 7 rather than a comparison between different approaches. This is because a fair comparison between different template protection schemes is very difficult, or even impossible, due to several factors such as the choice of biometric modality, fusion method, datasets, and evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Equal Error Rate (EER) (\%)**} \\ \hline  & \multicolumn{6}{c}{**Baseline (Feature Extraction without Hashing)**} \\ \hline  & **AR** & **Ethnic** & **Facescrub** & **IMDB Wiki** & **Pubfig** & **YTF** & **Average** \\ \hline
**Face** & 4.23 & 4.06 & 1.58 & 4.79 & 3.50 & 11.84 & 5.00 \\
**Periocular** & 6.77 & 5.61 & 3.13 & 6.53 & 5.48 & 15.16 & 7.11 \\
**Fused** & 4.67 & 5.29 & 1.75 & 5.12 & 4.07 & 13.56 & 5.74 \\ \hline  & \multicolumn{6}{c}{**PSMoT (\(m=4096,\ q=32\))**} \\ \hline  & **AR** & **Ethnic** & **Facescrub** & **IMDB Wiki** & **Pubfig** & **YTF** & **Average** \\ \hline
**Face** & 10.71 & 15.98 & 5.89 & 16.92 & 14.93 & 22.66 & 14.51 \\
**Periocular** & 15.36 & 16.85 & 10.40 & 18.69 & 16.31 & 22.73 & 16.72 \\
**Fused** & 5.90 & 7.48 & 2.16 & 5.97 & 4.68 & 14.55 & 6.79 \\ \hline  & \multicolumn{6}{c}{**CSMoFN (PSMoT + MDC)**} \\ \hline  & **AR** & **Ethnic** & **Facescrub** & **IMDB Wiki** & **Pubfig** & **YTF** & **Average** \\ \hline
**Face** & 9.62 & 15.34 & 5.07 & 15.64 & 12.68 & 20.61 & 13.16 \\
**Periocular** & 11.75 & 16.56 & 9.43 & 17.40 & 15.43 & 21.38 & 15.32 \\
**Fused** & 5.64 & 7.40 & 2.12 & 5.97 & 4.56 & 14.33 & 6.67 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study for each module (baseline, PSMoT, and CSMoFN).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Modalities** & **Fusion** & **Remarks** \\ \hline  & & & **Methods:** End-to-end deep-learning-based cancelable biometrics scheme with three modules (feature extraction and fusion module, permutation SoftmaxOut transformation module, multiplication-diagonal compression module). \\ Our proposed & Face, & Feature-level & **Revocability:**\(\blackdiamond\) \\ method & & region & **Unlinkability:**\(\blackdiamond\) \\  & & & **Non-invertibility:**\(\blackdiamond\) \\  & & & **Accuracy performance:** EER = 2.12\% (Facescrub dataset), EER = 6.67\% (average over six datasets) \\ \hline  & Face, eye & & **Methods:** Feature extraction is performed with multiple CNNs. After fusion of multiple deep features, BTP transformation is performed with \\ Abdellatef & region, & & **bioconvolving encryption. \\  & nose & & **Revocability:**\(\blackdiamond\) \\  & region, & & **Unlinkability:**\(\blackdiamond\) \\  & mouthregion & & **Non-invertibility:**\(\blackdiamond\) \\  & & & **Accuracy performance:** Accuracy = 93.4\% (PaSC dataset) \\ \hline  & & & **Methods:** Deep feature extraction and binarization using CNN from \\  & & & & multimodal modalities. A random component is selected from the generated features and used as a transformation key. The transformed templates are \\ [PERSON] et al. [24] & Face, iris & Feature-level & converted to a secure sketch via an FEC decoder and cryptographic hashing. \\
**Recvability:\(\blackdiamond\)** & **Unlinkability:**\(\blackdiamond\) \\  & & & **Non-invertibility:**\(\blackdiamond\) \\  & & & **Accuracy performance:** Accuracy = 99.16\% (stolen key scenario) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of works related to cancelable multimodal biometric systems.