# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 4 Experiments and Results
### Results  
This section presents the results of tests performed using the designed neural network architectures for the given data. The main goal of the conducted research was to find the optimal architecture capable of correctly classifying the gaze focus point. The resulting models, trained with training sets during the course of the experiments, were embedded in the specially designed _EyeTracker_ application, allowing their performance to be tested in real-time. Each experiment was conducted on two datasets. The first (denoted as D1) contained images acquired from one individual while the second (denoted as D2) contained images collected from four individuals, and one individual performed the test twice with contact lenses and with glasses, as mentioned in Section 3.3. Every network architecture was trained up to 70 epochs. The first test was aimed at checking if the model will work better after specific preprocessing. The first subset consisted of original grayscale images, and the second of the images was additionally sharpened by using an algorithm available in the OpenCV library. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Number of Filters** & **Filter Size** & **Step** \\ \hline LLE1, LRE1 & 2 \(\times\) 96 & 6 \(\times\) 3 & 2 \\ LLE3, LRE3 & 2 \(\times\) 384 & 3 \(\times\) 3 & 1 \\ LLE4, LRE4 & 2 \(\times\) 256 & 3 \(\times\) 3 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of convolutional layers for an experiment using both eyes images as neural network inputs. This experiment was performed based on images from the dataset containing data collected from one person (D1) and using ARCH-LRE architecture. The results of the experiment are presented in Table 3 (columns two and three). The second experiment aimed at comparing the results for both datasets D1 and D2. Since the first experiment already proved that the network works better when sharpened images are used, only sharpened images from D2 were used. The results are presented in the last column of Table 3.  
The following experiment performed the neural network learning process using an architecture that takes entire face images as input parameters. The schema of the developed ARCH-F architecture used in this experiment is presented in Section 3.4.2. The results of that experiment for Datasets 1 and 2 are presented in Table 4.  
The last two experiments aimed to check if it is possible to use images of only one eye and achieve results comparable to the more complicated network utilizing two eyes. The results of these experiments for left and right eyes and both datasets are presented in Table 5. \begin{table}
\begin{tabular}{c c c} \hline
**Epoch** & **Dataset 1** & **Dataset 2** \\ \hline
1–10 & 11.80\% & 7.42\% \\
11–20 & 15.20\% & 38.07\% \\
21–30 & 22.74\% & 72.66\% \\
31–40 & 39.88\% & 80.02\% \\
41–50 & 61.81\% & 83.99\% \\
51–60 & 77.00\% & 66.61\% \\
61–70 & 81.81\% & 80.81\% \\ \hline \end{tabular}
\end{table} 
Table 4: Results for ARCH-F architecture—averaged accuracy for the range of training epochs. \begin{table}
\begin{tabular}{c c c c} \hline
**Epoch** & **Original Images** & **Sharpened Images** & **Sharpened Images** \\
**(D1)** & **(D1)** & **(D2)** \\ \hline
1–10 & 26.01\% & 27.52\% & 16.25\% \\
11–20 & 41.25\% & 45.92\% & 30.73\% \\
21–30 & 56.48\% & 59.12\% & 43.04\% \\
31–40 & 67.32\% & 68.34\% & 53.06\% \\
41–50 & 73.62\% & 72.70\% & 54.46\% \\
51–60 & 76.52\% & 77.68\% & 63.28\% \\
61–70 & 77.24\% & 81.61\% & 73.28\% \\ \hline \end{tabular}
\end{table} 
Table 3: Results for original and sharpened images (the latter for both D1 and D2)—averaged accuracy for the range of training epochs.