# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 4 Conclusions  
This work proposes a methodology for the improvement of 3D head reconstruction. The primary application of these 3D reconstruction algorithms is to create one's head model using selfie video as the data source, so the improvements of the algorithms are directed and somewhat constrained by the origin of the data. The adaptation of the algorithms to process this type of data is the scope of this research.  
The evaluation of 3D head reconstruction improvements was performed using 19 videos of a mannequin head. Reconstruction quality depends on the amount of semantic noise and reconstruction errors of the head. The influence of the semantic noise on the quality of the head model is mainly reflected by the results of Evaluation Case 1, where residuals are calculated in the entire area of the reference head. These results show that the baseline algorithm is improved 12 times by introducing all improvements--elimination of the features in the background and selecting a subset of the best quality frames from the complete set of frames. The same modifications of the algorithm presented the largest improvement (nearly seven times) in Evaluation Case 2, where residuals are calculated only in the face area of the reference head. The latter experimental case reflects the precision of the head reconstruction.  
The selection of a subset of best quality images is based on image ordering by similarity. Comparative evaluation of feature sources (layer of CNN) and dimensionality-reduction techniques used to order images by similarity showed that using t-Distributed Stochastic Neighbor Embedding (t-SNE) in combination with features from the 14 th convolutional layer (out of 25) of CNN to order images by similarity in \(\mathbb{R}^{3}\) provides the largest number of correctly predicted images (75%), being the closest to the reference image. A comparison of single-step and two-step approaches of head detection showed that in this case (combination of 14 th layer features and t-SNE), the approaches perform similarly.  
The evaluation of the reduction of computational demand shows that the introduction of all the proposed improvements compared to the baseline algorithm reduced the frame number needed to process by two times, reduced the number of image matches required to perform by six times, reduced the average number of feature points in images by 1.4 times, while the point count in the facial area of the head point cloud was reduced by only 1.2 times and provided the highest precision of the head reconstruction.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12010229/s1](https://www.mdpi.com/article/10.3390/app12010229/s1)]([https://www.mdpi.com/article/10.3390/app12010229/s1](https://www.mdpi.com/article/10.3390/app12010229/s1)), Figure S1: Sparse point clouds of reconstructed heads by using all reconstruction pipelines discussed in the article.  
Conceptualization, D.M. and A.S.; methodology, D.M. and A.S.; software, D.M.; validation, D.M. and A.S.; formal analysis, D.M.; investigation, D.M. and A.S.; resources, A.S.; data curation, D.M.; writing--original draft preparation, D.M.; writing--review and editing, D.M. and A.S.; visualization, D.M.; supervision, A.S.; project administration, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy issues.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript: