# UFaceNet: Research on Multi-Task Face Recognition Algorithm Based on CNN
## 4 Experiment
### Results  
(1) Face Verification: We calculated the facial features on the LFW dataset and found the similarity between the images. As shown in Figure 8, the left half of the area is the test group of the same identity, and the right half of the area is the test group of different identities. We compare the accuracy of the model when calculating the face center vector with different numbers of images. As shown in Table 3, the accuracy can be 99.9% when using three image calculation centers for verification, and the accuracy can reach 100% when using more than four image calculation centers. We compared the accuracy of different algorithms on the LFW dataset. The result is shown in Table 4. We compared the calculations of different models. As shown in Table 5, UFaceNet is far superior in the calculation to other models. It can be seen that UFaceNet guarantees the accuracy of face verification while achieving network acceleration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Dataset** & **Images** \\ \hline \multirow{6}{*}{Basic Facial Point Detection} & CelebFaces Attributes Dataset [39; 40; 41] & 202,599 \\  & WIDER Face [42] & 32,203 \\  & CelebFaces Attributes Dataset & 202,599 \\  & 300 Faces In-the-Wild Challenge [43; 44] & 600 \\  & LFPW & 1132 \\  & HELEN & 348 \\  & AFW & 205 \\  & IBUG & 135 \\  & HWLD dataset & 400,000 \\  & MS-Celeb-1M [45] & 5,000,000 \\  & Labeled Faces in the Wild [46] & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tasks and datasets. (2) Accurate Facial Point Detection: We compared different facial point detection algorithms on the 300-W test data. As shown in Tables 6 and 7, common corresponds to the test set portion of the LFPW and HELEN datasets, challenge corresponds to the IBUG dataset, and fully corresponds to all test sets. It can be seen that the facial point detection method of the UFaceNet model has achieved better results than other existing facial point detection algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Dimension** & **Accuracy** & **FAR = 0** \\ \hline DeepFace [34] & 4096 & 0.973 & 0.463 \\ DeepID [35] & 160 & 0.99 & 0.693 \\ WebFace [47] & 320 & 0.977 & - \\ FaceNet [36] & 512 & 0.996 & - \\ VGG & 4096 & 0.97 & 0.61 \\ LightCNN-4 [37] & 256 & 0.979 & 0.79 \\ LightCNN-9 [37] & 256 & 0.988 & 0.95 \\ LightCNN-2p [37] & 256 & 0.993 & 0.975 \\ UFaceNet & 256 & 0.986 & 0.965 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Face Verification Accuracy Between Different Models with LFW. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Calculation** & **Parameter** &
\begin{tabular}{c} **Number of Network** \\ **Layers** \\ \end{tabular} \\ \hline FaceNet & 1600 & 140 & 11 \\ LightCNN-29 & 2300 & 12.6 & 29 \\ LightCNN-9 & 1900 & 5.5 & 9 \\ LightCNN-4 & 1300 & 4.1 & 4 \\ WebFace & 774 & 5 & 10 \\ UfaceNet & 589 & 300 & 19 \\ UfaceNet(face) & 297 & 8.2 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Calculation and Parameters of Different Models. Figure 8: UFaceNet faces the verification test results and similarity calculation with LFW. detection models. We also perform facial point detection tests on the LFW dataset. The result is shown in Figure 9.  
(3) Liveness Detection: We conducted training and testing of liveness detection on the HWND dataset constructed in this paper. We tested the effect of different numbers of random skin clipping regions on 3000 images on the HWLD dataset test set. As shown in Table 8, the accuracy can be improved as the number of cropping areas increases. (4) Face Detection: Intersection-over-Union (IoU) is an evaluation parameter in the target detection, which is the overlap ratio between the predicted bounding box and the real bounding box. When the IoU is greater than 0.5, it is considered that the target is detected. We tested the accuracy of the algorithm for different IoUs with LFPW and WIDEF Faces.