## 4 The Proposed Finger Photo Color Net (FPCN)
### Results  
The experimental results related to the correlation analysis demonstrate that the color spaces considered in this study complement each other. Pearson correlation confirms that the color spaces to be fused are uncorrelated. From Table 2, it is clear that HSV and LAB images exhibit a Pearson correlation coefficient of 0.015 and \(-\)0.031, respectively, which suggests no correlation. Furthermore, RGB and CMY images are negatively correlated. The general trend found from this analysis supports the initial hypothesis that color spaces provide enough diversity to be exploited in a fusion strategy. Table 3 illustrates the EER% of the CNNs under study when individually fine-tuned on a single color space. Findings support the initial hypothesis about the diversity in the information provided by various color spaces, which can enhance the performance of the overall system. The lowest EER of 0.96% is achieved by the MobileNet-V3 trained using HSV images. Further, 1.39% and 1.84% are the EER achieved by YCbCr and CMY, respectively. ResNet-18 and ResNet-34 perform well with RGB, YCbCr, and CMY color spaces. ResNet34 reached an EER of 1.19% using HSV. Table 4 compares CNN models individually trained by using all color spaces as well as only by using RHY, the proposed late fusion framework, and the state-of-the-art. Results are reported in terms of EER%, average accuracy after repeating each experiment three times, standard deviation, F1, and 10-fold cross-validation. Individual CNNs trained on all color spaces (e.g., RHYLXC) generally do not exhibit competitive results. In particular, ResNet-34 and Densenet-121 achieved an EER of 6.12% and 6.38%. When trained on RHY, Densenet-121 and Resnet-34 achieved an EER of 3.01% and 3.54%. By using the proposed late fusion strategy, the combination RHYC achieved an EER of 0.841%. Late fusion with selected best net models outperformed the current state-of-the-art. Figure 7a shows the DET curve of the proposed late fusion strategy. RHYXC and RHY achieved an EER of 0.961%, with BPCER of 4.88% at APCER = 0.1%, and 0.864% with BPCER of 3.5% at APCER = 0.1%, respectively. The lowest EER of 0.841% is obtained by RHYC with BPCER of 2.67% at APCER = 0.1%. Figure 7b shows the DET curve of individual CNNs trained on RHY color spaces. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RGB** & **HSV** & **LAB** & **XYZ** & **YCbCr** & **CMY** \\ \hline RGB & 1.000 & 0.015 & \(-\)0.031 & 0.998 & 0.324 & \(-\)0.983 \\ HSV & 0.015 & 1.000 & 0.017 & \(-\)0.453 & \(-\)0.037 & \(-\)0.010 \\ LAB & \(-\)0.031 & 0.053 & 1.000 & \(-\)0.013 & 0.334 & \(-\)0.004 \\ XYZ & 0.995 & 0.026 & \(-\)0.013 & 1.000 & 0.333 & \(-\)0.993 \\ YCbCr & 0.324 & 0.011 & 0.335 & 0.240 & 1.000 & \(-\)0.621 \\ CMY & \(-\)0.990 & \(-\)0.006 & \(-\)0.007 & \(-\)0.993 & \(-\)0.283 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson correlation between color spaces. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Color Space** & **AlexNet** & **ResNet-18** & **ResNet-34** & **VGG16** & **VGG19** & **GoogLeNet** & **MobileNetV3** & **DenseNet-121** \\ \hline RGB & 2.875 & 1.381 & 1.454 & 4.012 & 3.274 & 2.124 & 2.024 & 1.274 \\ XYZ & 3.87 & 5.124 & 3.765 & 5.611 & 5.612 & 6.248 & 3.164 & 4.127 \\ YCbCr & 2.471 & 1.918 & 2.472 & 4.37 & 4.374 & 5.312 & 1.394 & 3.041 \\ HSV & 3.214 & 2.412 & **1.196** & **2.487** & **2.847** & 2.471 & **0.967** & 1.671 \\ LAB & 11.471 & 4.974 & 5.972 & 8.87 & 9.69 & 8.574 & 4.974 & 4.472 \\ CMY & 4.124 & 2.874 & 3.412 & 4.102 & 3.128 & 3.481 & 2.851 & 1.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the individual color spaces in terms of EER%. Estimating the liveness of RGB finger photos acquired with a smartphone under unstable illumination conditions is a complex task. Results suggest that characteristics of indoor and outdoor brightness conditions that were not captured by RGB can be captured by a combination of V and Y components in the HSV and YCbCr color spaces, respectively.