# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 3 Results and Discussion  
This work presents and evaluates a methodology for the improvement of the 3D head reconstruction process. The methodology is created keeping in mind that 3D reconstruction algorithms are intended for use in creating head models from selfie videos, and the models will most likely be used to make head measurements in order to select a suitable size of head wearables (hats, helmets, eyeglasses, etc.). This application of the algorithm forces the exploitation and respect of the properties and constraints of such data. The adaptation of algorithms to process this kind of data was the scope of this research. Identified factors that may negatively affect the reconstruction process and quality of the reconstructed head model are as follows: changing background (non-static scene), motion blur, defocus and rolling shutter distortions, head out of frame limits, and excess of redundant frames, which only slows down the reconstruction process. The primary sources of 3D head reconstruction improvements are--increase the reconstruction quality and reducing the number of required computations. The quality of reconstruction is defined by two components--reconstruction errors of the head and the amount of semantic noise. Thus, the approaches of quality improvement are to reduce both of the mentioned components. Semantic noise is reduced by minimizing non-head points in the reconstructed model, so the reconstructed scene includes only head points (this is mainly reflected by the results of Evaluation Case 1). Reconstruction errors of the head are reduced by suppressing factors that deteriorate the reconstruction precision (this is reflected by the results of Evaluation Case 2). The reduction of semantic noise leads to an easier localization of the head feature points, where anchors may be attached for measurements; reduced reconstruction errors provide a more precise head model and thus more accurate and reliable measurements. Semantic noise reduction is achieved by removing other objects (background information) from the initial head images. Reduction of reconstruction errors is achieved by increasing the image quality used for reconstruction, i.e., by selecting and using images of the highest quality. Images with higher quality (here, quality is mainly defined by the amount of motion blur and defocus) allow for more precise reconstruction of the head. Reduction of computational demand is achieved by these solutions: reducing the image number used to reconstruct the model by discarding redundant frames and reducing the feature number in images (leaving only features related to the head). In summary, the required key modifications of the general-purpose 3D reconstruction algorithm in order to improve 3D head reconstruction from selfie videos are the elimination of image background and selection of the highest quality frames. The proposed modifications to the general-purpose 3D reconstruction algorithm were introduced gradually, and their influence on the reconstruction process was evaluated in the reconstruction experiments. The gradual introduction resulted in three major branches of modified reconstruction algorithms and a total of six minor branches of algorithms: _Pipeline_ 2 [a! b], _Pipeline_ 3 [a! b], and _Pipeline_ 4 [a! b]. Two basic experiments were designed and used to perform a comparative evaluation of the algorithms. One experiment was for the evaluation of the core part components of _Pipeline_ 4 (results in Tables 3 and 4). The second experiment evaluated the general-purpose 3D reconstruction algorithm and its three major modifications we proposed in the reconstruction of the head from selfie videos (results in Table 5). For evaluation, experimental data were collected. The dataset consists of 19 test videos of the mannequin head and the reference head model. The reference head model was constructed from high-quality photographs with some manual input of the operator to increase the quality of the head model. For the test data, the head was captured in such a way that imitates selfie videos.