A relative 3D scan and construction for face using meshing algorithm

We propose and evaluate a three-dimensional (3D) face recognition approach that applies the speeded up robust feature (SURF) algorithm to the depth representation of shape index map, under real-world conditions, using only a single gallery sample for each subject. First, the 3D scans are preprocessed, then SURF is applied on the shape index map to find interest points and their descriptors. Each 3D face scan is represented by keypoints descriptors, and a large dictionary is built from all the gallery descriptors. At the recognition step, descriptors of a probe face scan are sparsely represented by the dictionary. A multitask sparse representation classification is used to determine the identity of each probe face. The feasibility of the approach that uses the SURF algorithm on the shape index map for face identification/authentication is checked through an experimental investigation conducted on Bosphorus, University of Milano Bicocca, and CASIA 3D datasets. It achieves an overall rank one recognition rate of 97.75%, 80.85%, and 95.12%, respectively, on these datasets.

OBJECTIVE To compare the registration accuracy of three-dimensional (3D) facial scans for the design of full-arch implant supported restoration by five methods and to explore the suitable registration method. METHODS According to the criteria, ten patients with maxillary edentulous jaw or end-stage dentition requiring implant supported restorations were enrolled in this study. A special rim with individual feature marks reflected appropriate occlusal relationship and esthetic characteristics was made for each patient. Both 3D facial scan data of natural laughter and with opener traction to expose the teeth or occlusal rim of each patient were acquired by facial scan and input to the digital analysis software Geomagic Qualify 2012. The dataset was superimposed by five different methods: seven facial anatomical landmark points alignment, facial immobile area alignment (forehead and nasal area), facial anatomical landmark points and immobile area combining alignment, facial feature points alignment, facial and intraoral feature points alignment with the same local coordinate system. The three-dimensional deviation of the same selected area was calculated, the smaller the deviation, the higher the registration accuracy. The 3D deviation was compared among the three registration methods of facial anatomical landmark points, facial immobile area alignment and the combination of the above two methods. Friedman test was performed to analyze the difference among the three methods (α=0.05). The effect of the aid of the facial and intraoral feature points were evaluated. Paired t test were performed to analyze the difference (P<0.05). RESULTS The average three-dimensional deviation of the selected area after alignment with the facial anatomical landmarks was (1.501 2±0.406 1) mm, significantly larger than that of the facial immobile area best-fit alignment [(0.629 1±0.150 6) mm] and the combination of the two methods[(0.629 1±0.150 6) mm] (P < 0.001). The aid of the facial feature points could significantly reduce the deviation (t=1.001 3, P < 0.001). There was no significant statistical difference in the remaining groups. CONCLUSION The forehead area of the 3D facial scan can be exposed as much as possible. The establishment of facial characteristic landmark points and the use of the invariant area alignment can improve the accuracy of registration. It should be clinically feasible to apply three-dimensional facial scan to the design of full-arch implant supported restoration with the registration of the immobile area on the face especially the forehead area.

3D face identification based on the detection and comparison of keypoints of the face is a promising solution to extend face recognition approaches to the case of 3D scans with occlusions and missing parts. In fact, approaches that perform sparse keypoints matching can naturally allow for partial face comparison. However, such methods typically use a large number of keypoints, locally described by high-dimensional feature vectors: This, combined with the combinatorial number of keypoint comparisons required to match two face scans, results in a high computational cost that does not scale well with large datasets. Motivated by these considerations, in this paper, we present a 3D face recognition approach based on the meshDOG keypoints detector and local GH descriptor, and propose original solutions to improve keypoints stability and select the most effective features from the local descriptors. Experiments have been performed to assess the validity of the proposed optimizations for stable keypoints detection and feature selection. Recognition accuracy has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face identification solutions based on 3D keypoints.

The use of three-dimensional (3D) facial scans for facial analysis is increasing in maxillofacial treatment. The aim of this study was to investigate the consistency of two-dimensional (2D) and 3D facial analyses performed by multiple raters. Six men and four women (25–36-year-old) participated in this study. The 2D images of the smiling and resting faces in the frontal and sagittal planes were obtained. The 3D facial and intraoral scans were merged to generate virtual 3D faces. Ten clinicians performed facial analyses by investigating 14 indices of 2D and 3D faces. Intra- and interrater agreements of the results of 2D and 3D facial analyses within and among the participants were evaluated. The intrarater agreement between the 2D and 3D facial analyses varied according to the indices. The highest and lowest agreements were found for the dental crowding index (0.94) and smile line curvature index (0.56) in the frontal plane, and Angle's classification (canine) index (0.98) and occlusal plane angle index (0.55) in the profile plane. In the frontal plane, the interrater agreements were generally higher for the 3D images than for the 2D images, while in the profile plane, the interrater agreements were high in the Angle's classification (canine) index however low in the other indices. Several occlusion-related indices were missing in the 2D images because the posterior teeth were not observed. Esthetic analysis results between 2D and 3D face images can differ according to the evaluation indices. The use of 3D faces is recommended over 2D images to increase the reliability of facial analyses, as it can fully assess both esthetic and occlusion-related indices.

In this paper, we present an automatic point matching method to overcome the dense point alignment of 3D face scans. We adopt TPS (thin plate spline) transformation to model the deformation of different 3D faces, because TPS is a type of non-rigid transformation with good smooth property and suitable for formulating the complexities of human facial morphology. Generally, TPS is derived from the interpolation between two sets of aligned controlling points. To obtain fully automatic point matching method, a random point selecting method is proposed to get the controlling points of 3D faces. Integrating the point generating method with an iterative closest point searching strategy, we achieve a point-to-point alignment solution for dense 3D face scans. The point matching tests on BJUT 3D face database reveal that the TPS based method has flexible shape deformation ability. Comparing with the hand-selecting controlling point method our method has better point matching accuracy and stability. It is proved that the presented method is efficient for dense point registration with non-rigid deformation.

Recently, the integration of 3D face scanning into smartphones has raised vast interest in plastic surgery. With the release of smartphones featuring 3D face scanning technology, users now can capture detailed 3D models of their faces using their smartphones. However, trueness and precision of this system is less well established. PubMed, Cochrane Library, Embase, ScienceDirect, Scopus, and Web of Science databases were searched for studies evaluating 3D scanning of smartphone devices and conventional 3D imaging systems from January 1, 2017, to June 1, 2023. A qualitative systematic review was conducted by two review authors after independently selecting studies, extracting data, and assessing the risk of bias of included studies. A total of 11 studies were included, all focusing on the accuracy of smartphone 3D facial scanning. The results show that although smartphones perform poorly on deep and irregular surfaces, they are accurate enough for clinical applications and have the advantage of being economical and portable. Smartphone-based 3D facial scanning has been basically validated for clinical application, showing broad clinical application prospects in plastic surgery. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Analysis of the influence of the facial scanning method on the transfer accuracy of a maxillary digital scan to a 3D face scan for a virtual facebow technique: An in vitro study

The first handicap in 3D faces recognizing under unconstrained problem is the largest variability of the visual aspect when we use various sources. This great variability complicates the task of identifying persons from their 3D facial scans and it is the most reason that bring to face detection and recognition of the major problems in pattern recognition fields, biometrics and computer vision. We propose a new 3D face identification and recognition method based on Gappy Wavelet Neural Network (GWNN) that is able to provide better accuracy in the presence of facial occlusions. The proposed approach consists of three steps: the first step is face detection. The second step is to identify and remove occlusions. Occluded regions detection is done by considering that occlusions can be defined as local face deformations. These deformations are detected by a comparison between the input facial test wavelet coefficients and wavelet coefficients of generic face model formed by the mean data base faces. They are beneficial for neighborhood relationships between pixels rotation, dilation and translation invariant. Then, occluded regions are refined by removing wavelet coefficient above a certain threshold. Finally, the last stage of processing and retrieving is made based on wavelet neural network to recognize and to restore 3D occluded regions that gathers the most. The experimental results on this challenging database demonstrate that the proposed approach improves recognition rate performance from 93.57 to 99.45 % which represents a competitive result compared to the state of the art.

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings. Significance: Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research.

Objective The aim of the current study was to investigate correlations between measurements on 3D facial soft tissue scan and CBCT skeletal structures. Methods The study sample consisted of 20 subjects living in Beijing area. A 3D face scan analysis including two distance values and 22 angle values based on 14 landmarks, and the corresponding CBCT analysis with three distance values and 9 angle values were performed for all subjects. All measurements were operated twice by the same researcher. Then, the reproducibility was evaluated by intraclass correlation coefficient (ICC), and the correlations were evaluated by Pearson correlation coefficient analysis. Besides, the thickness of facial soft tissue at relative points to explore possible reasons between the correlations. Results The reproducibility of measurements related to soft tissue gonion is medium. For sagittal measurement, medium correlations were found between soft tissue subspinale and hard tissue point A, as well as the relative position of maxilla and mandible. For vertical measurements, soft tissue gonions, mandibular plane angles showed medium correlations with hard tissue, and medium correlation were found between anterior upper and lower facial heights with corresponding hard tissue measurements and hard tissue mandibular plane angle. And the thickness of facial soft tissue at subspinale was larger than that at alare. Conclusions Soft tissue measurements of alare, the relative position between maxilla and mandible, upper anterior facial height and lower anterior facial height are reliable in providing a soft tissue diagnosis reference to the CBCT analysis. While the measurements related to soft tissue gonion should be used with caution. Key words: Three-dimensional facial soft tissue scan; CBCT; Correlation assessment between soft tissue and hard tissue; Skeletal class Ⅲ

Abstract-In order to eliminate the effect of the facial expression and illumination condition, as well as to speed up the recognition procedure, we propose a face recognition approach based on sparse representation. First, preprocessing and segmenting the face area from three dimensional (3D) face scans, we also apply coarse to fine registration to ensure the alignment of range images; second, mapping the 3D model to range image through a kind of geometry-based resampling method; finally, employ sparse representation classification method to identify 3D face. The experiment results in actual 3D face database demonstrate the effectiveness of the proposed method.

The impact of natural movement/sway while standing still during the capture of a 3D face model for biometric applications has previously been believed to have a negligible impact on biometric performance. Utilizing a newly captured dataset this paper demonstrates a significant negative impact of standing. A 0.5 improvement in d' (test of correct/incorrect match distribution separation) per 3D face region and noticeable improvement to match distributions are shown to result from eliminating movement during the scanning process. By comparing these match distributions to those in the FRGC dataset this paper presents an argument for improving the accuracy of 3D face models by eliminating motion during the capture process.

For restorative demands, increased vertical dimension of occlusion (VDO) is sometimes necessary, as facial changes can be perceptible. This study aimed to evaluate the perception of facial change due to increased VDO and associated soft tissue changes using 3D-scanned facial images. Forty participants with healthy dentition and no loss of VDO were recruited. The VDO was increased by 2, 4, 6 and 8 mm on a semi-adjustable articulator after maxillomandibular impressions. Facebow transfers were taken for each participant to create a personalised device. 3D facial scans were taken with the mandibular occlusal device in place, creating facial capture files from frontal, lateral and anteroposterior views for each scanned data. Sixty evaluators were recruited to consecutively cross-compare maximal intercuspal captures with increased VDO. To evaluate soft tissue changes with increased VDO, facial measurements, total face height, lower face height, lip width, lip height and nasolabial angle were measured thrice in 3D scan images using computer-aided design software. Statistical analysis entailed one-way repeated-measures analysis of variance (α = 0.05). A 4-mm increase in the VDO showed the highest perceptibility for facial change, varying among evaluator groups. Total face height, lower face height and nasolabial angle increased with VDO, while lip width and height decreased. Each soft tissue measurement differed significantly (p < 0.05). For 3D-scanned facial changes, a 4-mm increase in VDO was the most perceptible difference. Increased VDO affects facial changes.

This paper presents the Discriminative Generalized Hough Transform (DGHT) as a technique to localize landmarks in 3D face scans. While the DGHT has been successfully used for the detection of landmarks in 2D and 3D images this work extends the framework to be used with triangle meshes for the first time. Instead of edge features and their respective gradient direction, the relative positions and orientations of the mesh faces are utilized to describe the geometric structures which are relevant for the detection of a specific landmark. Implementing a coarse-to-fine strategy at first a decimated version of the mesh is used to locate the global region of the point of interest, followed by more detailed localizations on higher resolution meshes. The utilized shape models are created in an automated, discriminative training process which assigns individual weights to the single model points, aiming at an increased localization rate. The technique has been applied to detect 38 anthropometric facial landmarks on 99 3D face scans. With an average error of 1.9mm, the most accurate detection was performed for the right alare, the average error when considering all landmarks amounts to 5.1 mm.

Evaluating facial paralysis: A comparative study of 2D Emotrics and 3D scanning technologies.