# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## 5 Conclusions  
We proposed a passive face spoofing detection method designed to operate in a work time registration system under real-world constraints: variable lighting, low image quality (6 kB webp format), and no possibility to invest in additional hardware (e.g., infrared). Our approach effectively balances performance, flexibility, and computational efficiency, making it a viable solution for real-world employee authentication systems.  
The presented method is based on a stacking committee, where each member model evaluates different aspects of the photo. Experimental evaluation showed that the use of a stacking committee improves the accuracy of the entire system. We experimentally evaluated 240 different committees and different threshold parameters for the final classifier in the committee.  
Additionally, the use of a centering step in the CNN analysis improves the accuracy of that module, but not implementing that step only slightly affects the total accuracy of the system. The use of a committee should also increase the adaptability of the system to real-world conditions, as different methods can detect different types of spoofing attacks.  
The modular nature of our design supports future expansion by adding additional models. This will be the subject of our future work. Specifically, we plan to examine the possibility of adding new models to the committee, to evaluate grayscale vs. RGB photos as CNN-based network input, and to train the final neural network with different parameters.  
\begin{table}
\begin{tabular}{l c c} \hline
**Outcome** & **Authentic Faces** & **Spoof Faces** \\ \hline Authentic & 8 & 0 \\ \hline Spoof & 230 & 144 \\ \hline Not detected & 5 & 90 \\ \hline \end{tabular}
\end{table}
Table 6: Obtained results for the commercial system “A Personalized Benchmark for Face Anti-spoofing” for the small dataset (N = 477).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Small** & **Spoofing** & **Crowd** & **CelebA** \\ \hline Baseline & 90.64 \(\pm\) 2.77 & 95.37 \(\pm\) 1.10 & 96.83 \(\pm\) 0.28 & 98.59 \(\pm\) 0.38 \\ \hline Platt scaling & 86.35 \(\pm\) 2.13 & 94.49 \(\pm\) 1.79 & 97.00 \(\pm\) 0.66 & 98.64 \(\pm\) 0.15 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of accuracy [%] obtained for the stacking ensemble consisting of our own methods (B,S,C,N) with and without Platt scaling with constant parameters and static random seed.  
Moreover, the acquisition and generation of new data specific to the industrial conditions of the employee registration environment are important aspects to further improve the system.  
Conceptualization, M.K. and R.K.; methodology, M.K. and R.K.; software, M.K. and R.K.; validation, M.K. and R.K.; formal analysis, M.K. and R.K.; investigation, M.K. and R.K.; resources, M.K. and R.K.; data curation, M.K. and R.K.; writing--original draft preparation, M.K. and R.K.; writing--review and editing, M.K. and R.K.; visualization, M.K. and R.K.; supervision, M.K. and R.K.; project administration, M.K. and R.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Centre for Research and Development of Poland (NCBiR), project nr POIR.01.01.01-00-1144/19.  
Not applicable.  
The data and source code used in the experiments are available at [[https://github.com/[PERSON]/FaceAuthenticityDetection](https://github.com/Stukeley/FaceAuthenticityDetection)]([https://github.com/Stukeley/FaceAuthenticityDetection](https://github.com/Stukeley/FaceAuthenticityDetection)).  
Author [PERSON] was employed by the company inEwi, sp. z o. o. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.