# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.3 Comparison with Existing Segmentation Algorithms  
In this experiment, deep-learning-based algorithms FCN-8s [11], U-Net [13], FREDNet [14], PI-Unet [15], and MFFIris-Unet [18] were each selected to be trained and tested on multi-source heterogeneous iris datasets. The test results were compared with PPSegIris on several evaluation indexes. Four datasets were selected for the experiment, which were JLU-6.0, CASIA-Iris-Lamp-v4, UBIRIS.V2, and MICHE-I. Table 5 gives the evaluation indexes of different algorithms on four iris datasets. Table 6 gives the comparison of different algorithms in terms of Params, Storage Space, and FLOPs.  
Conclusions can be obtained as follows after the analysis of Tables 5 and 6:  
1. Compared with other methods, PFSegIris achieved higher segmentation accuracy on multi-source heterogeneous iris images, with mIoU reaching 97.38%, 97.15%, 96.69%, and 96.24% and F1-score reaching 98.68%, 97.71%, 97.29%, and 97.13% on four iris datasets of JLU-6.0, CASIA-Iris-Lamp-v4, UBIRIS.V2, and MICHE-I, respectively, further verifying the universal applicability of the algorithm proposed for multi-source heterogeneous iris segmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Params/M** & **Storage Space/GB** & **FLOPs/G** \\ \hline FCN-8s & 134.27 & 0.500 & 83.42 \\ U-Net & 31.03 & 0.116 & 62.06 \\ FRED-Net & 9.7 & 0.036 & 19.5 \\ PI-Unet & 2.96 & 0.011 & 1.60 \\ MFFIris-Unet & 1.95 & 0.007 & 0.74 \\
**PFSegIris** & **1.86** & **0.007** & **0.65** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different algorithms in terms of Params, Storage Space, and FLOPs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dataset** & **mIoU/\%** & **F1-Score/\%** & **Average Time/s** \\ \hline \multirow{4}{*}{FCN-8s} & JLU-6.0 & 85.35 & 92.16 & 0.29 \\  & CASIA-Lamp & 83.24 & 90.73 & 0.28 \\  & UBIRIS.V2 & 77.72 & 86.85 & 0.15 \\  & MICHE-I & 77.29 & 87.67 & 0.19 \\ \hline \multirow{4}{*}{U-Net} & JLU-6.0 & 89.01 & 93.98 & 0.96 \\  & CASIA-Lamp & 87.45 & 92.04 & 0.92 \\  & UBIRIS.V2 & 82.21 & 91.77 & 0.61 \\  & MICHE-I & 81.54 & 90.25 & 0.58 \\ \hline \multirow{4}{*}{FRED-Net} & JLU-6.0 & 94.08 & 95.78 & 0.34 \\  & CASIA-Lamp & 92.50 & 94.63 & 0.31 \\  & UBIRIS.V2 & 91.89 & 93.84 & 0.28 \\  & MICHE-I & 91.03 & 93.15 & 0.25 \\ \hline \multirow{4}{*}{PI-Unet} & JLU-6.0 & 95.58 & 97.03 & 0.21 \\  & CASIA-Lamp & 94.35 & 96.68 & 0.18 \\  & UBIRIS.V2 & 92.33 & 95.52 & 0.27 \\  & MICHE-I & 93.67 & 94.26 & 0.35 \\ \hline \multirow{4}{*}{MFFIris-Unet} & JLU-6.0 & 96.17 & 97.56 & 0.15 \\  & CASIA-Lamp & 95.76 & 97.28 & 0.12 \\ \cline{1-1}  & UBIRIS.V2 & 95.32 & 96.62 & 0.11 \\ \cline{1-1}  & MICHE-I & 94.75 & 96.59 & 0.09 \\ \hline \multirow{4}{*}{**PFSegIris**} & JLU-6.0 & **97.38** & **98.68** & **0.12** \\ \cline{1-1}  & CASIA-Lamp & **97.15** & **97.91** & **0.10** \\ \cline{1-1}  & UBIRIS.V2 & **96.69** & **97.29** & **0.09** \\ \cline{1-1}  & MICHE-I & **96.24** & **97.13** & **0.06** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The evaluation indexes of different algorithms on four iris datasets.  
Compared with other network models, PFSegIris had a faster segmentation speed on multi-source heterogeneous iris images. It is an algorithm that can quickly segment multi-source heterogeneous iris.
3. Compared with the classic semantic segmentation methods and the latest lightweight iris segmentation methods, the proposed algorithm has fewer parameters, less storage space, and less computation with obvious application advantages.