## 6 Conclusions  
Finger photo technology can be used as a viable approach for touchless authentication in mobile devices. However, it is vital to understand the implications of spoofing attempts on finger photo recognition. The proposed research provides a mechanism for defense against finger photo spoofing. Different CNNs are trained individually using various color spaces and then integrated via deep fusion. Finger photo patches were used to train the deep networks to conserve texture information in a local region. Results demonstrate the superiority after combining different color spaces compared to the state-of-the-art. Findings show that when late fusion is performed, the scheme that integrates RHYC results in an EER of 0.841%. This supports the hypothesis that different color spaces complement each other, and combining the information provided by each of them can aid spoof representation and thus PAD. The proposed study is constrained by the limited publicly available finger photos spoof database with 64 subjects. Furthermore, the existing IIIT-D database device used for the acquisition of live and spoof samples was not of similar specification. Therefore, our future efforts will focus on creating a larger database for spoof finger photos using the same capture device for live and spoof. This will allow us to generalize our current framework. We will also explore methodologies such as activation maps to explain the deep models obtained in this work.  
Methodology, E.M.; software, A.V.; validation, E.M., A.V.; formal analysis, E.M., A.V.; investigation, E.M., A.V.; writing--original draft preparation, E.M.; writing--review and editing, E.M., A.V.; visualization, E.M., A.V.; supervision, E.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l r r r r r r r} \hline \hline  & \multicolumn{2}{c}{**RHYLC**} & \multicolumn{2}{c}{**RHYLC**} & \multicolumn{2}{c}{**RHYLC**} & \multicolumn{2}{c}{**RHYC**} & \multicolumn{2}{c}{**RHY**} \\ \hline  & \(\chi^{2}\) & \(p\) **\textless{} 0.05** & \(\chi^{2}\) & \(p\) **\textless{} 0.05** & \(\chi^{2}\) & \(p\) **\textless{} 0.05** & \(\chi^{2}\) & \(p\) **\textless{} 0.05** \\ \hline RHYLC & & 16.46 & \(4.9\times 10^{-5}\) & 35.53 & \(2.5\times 10^{-8}\) & 35.15 & \(5.7\times 10^{-8}\) \\ RHYLC & 16.46 & \(4.9\times 10^{-5}\) & & & 4.212 & 0.041 & 29.45 & \(4.2\times 10^{-12}\) \\ RHYC & 35.53 & \(2.5\times 10^{-8}\) & 4.212 & 0.041 & & 26.16 & \(3.1\times 10^{-7}\) \\ RHY & 35.15 & \(5.7\times 10^{-8}\) & 29.45 & \(4.2\times 10^{-12}\) & 26.16 & \(3.1\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: [PERSON]’s test.  
**Funding:** Marasco was partially supported by the National Science Foundation (NSF) under award CNS-1822094.  
**Data Availability Statement:** The two databases used in this study are IIITD Smartphone FingerSelfie Database V2 and the Spoofed Fingerphoto Database 1. The link to IIITD Smartphone FingerSelfie Database V2 is [[http://iab-rubric.org/index.php/spfd2](http://iab-rubric.org/index.php/spfd2)]([http://iab-rubric.org/index.php/spfd2](http://iab-rubric.org/index.php/spfd2)), last accessed on 26 September 2022. and; 2. The link to Spoofed Finger photo Database is [[http://iab-rubric.org/index.php/spoofed-fingerpho-to-database](http://iab-rubric.org/index.php/spoofed-fingerpho-to-database)]([http://iab-rubric.org/index.php/spoofed-fingerpho-to-database](http://iab-rubric.org/index.php/spoofed-fingerpho-to-database)) photo database, last accessed on 26 September 2022  
**Conflicts of Interest:** The authors declare no conflict of interest.