# The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking
## 3 Results
### Evaluating Model
#### 3.2.3 Face Scheme  
According to Table 2, all the experiments are selected for the face scheme, called Experiment 3-1, Experiment 3-2, Experiment 3-3, Experiment 3-4 and Experiment 3-5, respectively. Experiment 3-1 is designed to evaluate the performance by adjusting NoCL, and other parameters are set as Lr to \(1\times 10^{-5}\), FC512, and without BN. The results are shown in Figure 20. The result has the highest accuracy of 0.862 for C13. However, C5 to C8 performed the worst; the reason for this is that the network is not deep enough to learn features. In order to reduce the evaluation time, the following experiments will only be evaluated with C9 to C16. Experiment 3-2 is designed to observe the effect on the different FC based on the different types of fed images, and other parameters are set as NoCL to C13, Lr to \(1\times 10^{-5}\), and without BN. The results are shown in Figure 21. The accuracy of FC512 is only 0.001 less than FC4096. However, the parameter amount of FC512 is only 1/4 times that of FC4096, as shown in Figure 22. Therefore, in the case of the close accuracy, the FC512 with fewer parameters is a good choice. Figure 21: The accuracy of the face scheme for Experiment 3-2. Figure 20: The accuracy of the face scheme for Experiment 3-1. Experiment 3-3 evaluates the accuracies for different NoCL, and other parameters are set as NoCL to C13, Lr to \(1\times 10^{-5}\), and with BN. The results are shown in Figure 23. The highest accuracy is in the case of C13, comparing with Figure 21, the accuracy is slightly improved. Additionally, comparing with the single eye scheme and the double eyes scheme, the accuracy is slightly improved due to the network being deeper. Experiment 3-4 will evaluate the effect on the model when GAP replaces the fully connected layer for different NoCL, and other parameters are set as Lr to \(1\times 10^{-5}\), without BN. The results are shown in Figure 24. The highest accuracy is C12. The number of parameters of different NoCL for comparing FC512 with GAP is described in Figure 25. For example, in the case of C12, the number of parameters is reduced by about 1/4 compared to the FC512, but the accuracies are kept about the same. Compared with Figure 20, the accuracy decreases in case of the deeper network C14, C15 and C16. Additionally, compared with the single eye scheme and double eyes scheme, the accuracy is slightly improved due to the network being deeper. Figure 23: The accuracy of the face scheme for Experiment 3-3. Figure 22: The number of parameters of the face scheme for Experiment 3-2. Experiment 3-5 evaluates the effect for different NoCL, and other parameters are set as Lr to \(1\times 10^{-5}\), with GAP and BN. The results are shown in Figure 21.0 The C12 has the best accuracy rate of 0.833, and the execution time for a gaze estimating is 10.776 ms. Compared to Experiment 3-4 without BN, the accuracy increases by 0.05, as shown in Figure 21.1  
Figure 21.2 The accuracy of the face scheme for Experiment 3-5. Figure 21.3 The accuracy of the face scheme for Experiment 3-4. Figure 21.4 The number of parameters of the face scheme for replacing FC. Finally, the comparison of four experiments with good performance is of the face scheme shown in Figure 21.5 When GAP replaces the fully connected layer, the parameters reduced to 1/2 of the original. The accuracy increases to 0.883. The model architecture of the best performance includes 12 convolutional layers, BN and GAP, and the learning rate is set to \(1\times 10^{-3}\). Table 4 summarizes the results for the 3 proposed schemes. According to the single eye scheme, comparing FC512 without BN and FC512 with BN, there is no improvement in accuracy with BN. However, when using GAP, the result shows improvement in accuracy. Based on the evaluation results of the double eyes scheme, the accuracy of using BN is improved. In the face scheme, it is shown that the accuracy is significantly improved when using GAP and BN at the mean time. Overall, the highest accuracy is obtained by using GAP and BN together.