# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 3 Results and Discussion
In this research, the absolute scale of the models was not estimated. Determination of the absolute scale can be added in future research to create 3D models for absolute measurements. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Variant of 3D Reconstruction Pipeline 1**} \\ \cline{2-7}
**Attribute** & **1 (Baseline)** & **2a** & **2b** & **3a** & **3b** & **4a** & **4b** \\ \hline
**Images 2** & \(412\pm 86\) & \(412\pm 86\) & \(412\pm 86\) & \(412\pm 86\) & \(412\pm 86\) & \(412\pm 86\) & \(202\pm 6.0\) & \(202\pm 6.0\) \\
**Image matches 3** & \(12,792+2668\) & \(12,946\pm 2798\) & \(12,946\pm 2798\) & \(12,913\pm 2770\) & \(12,913\pm 2770\) & \(6207\pm 243\) & \(6207\pm 243\) \\
**Feature points 4** & \(21,108\pm 1167\) & \(6899\pm 1913\) & \(14,985\pm 4451\) & \(7046\pm 1953\) & \(15,248\pm 4507\) & \(6963\pm 1984\) & \(15,044\pm 4595\) \\
**Points in PC1** 5 & \(32,996\pm 12,957\) & \(29,376\pm 10,373\) & \(37,589\pm 15,721\) & \(34,389\pm 11,972\) & \(46,220\pm 18,279\) & \(14,027\pm 5919\) & \(17,290\pm 8317\) \\
**Points in PC2** 6 & \(13,322\pm 6600\) & \(15,054\pm 7809\) & \(20,207\pm 11,008\) & \(18,125\pm 9074\) & \(25,497\pm 13,100\) & \(8563\pm 4336\) & \(11,048\pm 6009\) \\ \hline \hline \end{tabular}  
* 3D reconstruction algorithms are explained in Table 1; 2 average number of frames (\(\pm\)SD) selected for the reconstruction; 3 average number of image matches (\(\pm\)SD) performed during reconstructions; 4 average number of feature points (\(\pm\)SD) extracted in images; 5, 6 average number of points (\(\pm\)SD) in point clouds of the full head and only in the face region, respectively.  
\end{table} 
Table 6: Descriptive statistics of experimental data. Some of the limitations of the presented experiment are that only the mannequin and a single head were used for the experimental data collection. Larger testing scenarios with more mannequins and even real people are of interest. An interesting question is how the proposed algorithms would work in the case of real faces. Insights that were made during the preparation and execution of the current experiment tell that there are factors that in, the case of reconstructing real faces, would help to improve the model's quality (lower errors), but there are also factors that may impair the automatic reconstruction process. Real faces could be reconstructed easier as they have various additional patterns that help to extract more distinct features in the face area. This improves the determination of the corresponding areas. Sources of reconstruction difficulties would reside in the capturing of real faces using a camera. Involuntary face movements, small or large, are inevitable. Some of them could be tolerated to a certain level. Larger movements need to be detected and eliminated. The solution could be to automatically detect face movements that can not be tolerated (if they have too much negative impact on the model's quality) and exclude a subset of the samples, or the user could be asked to recapture one's face. Modifications of the algorithms were designed keeping in mind that capturing one's own head is most convenient by making selfie videos using a smartphone. During a short filming period, the face can be kept sufficiently still. Another subject of improvement in further experiments could be the construction of a reference head model. A better reference model could be created using high-accuracy 3D scanners. This will be necessary in case absolute measurements need to be compared. In the current research, our approach to create the reference model using the photogrammetry pipeline without the proposed modifications that need to be tested does not prevent relative comparison of the models and evaluation of the modifications to reveal their relative influence. The quality of the reference model is maximized by using high-quality manually revised images.