Validation of the automatic tracking for facial landmarks in 3D motion captured images

Aim: The aim of this study was to validate the automatic tracking of facial landmarks in 3D image sequences captured using the Di4D system (Dimensional Imaging Ltd., Glasgow, UK). MATERIALS AND METHODS: 32 subjects (16 males; 16 females) range 18-35 years were recruited. 23 facial landmarks were marked on the face of each subject with a 0.5 mm non-permanent ink. The subjects were asked to perform three facial animations from the rest position (maximal smile, lip purse and cheek puff). Each animation was captured by a 3D stereophotogrammetry video system (Di4D). A single operator digitized landmarks on captured 3D models and the manual digitised landmarks were compared with the automatic tracked landmarks. To investigate the accuracy of manual digitisation, the same operator re-digitized 2 subjects (1 male and 1 female). RESULTS & CONCLUSION: The discrepancies in x, y and z coordinates between the manual digitised landmarks and the automatic tracked facial landmarks were within 0.5 mm and the mean distance between the manual digitisation and the automatic tracking of corresponding landmarks using tracking software was within 0.7 mm which reflects the accuracy of the method( p value was very small). The majority of these distances were within 1 mm. The correlation coefficient between the manual and the automatic tracking of facial landmarks was 0.999 in all x, y, and z coordinates. In conclusion, Automatic tracking of facial landmarks with satisfactory accuracy, would facilitate the analysis of the dynamic motion during facial animations

A semi-automatic three-dimensional technique using a regionalized facial template enables facial growth assessment in healthy children from 1.5 to 5.0 years of age.

Objectives To develop a semi-automatic technique to evaluate normative facial growth in healthy children between the age of 1.5 and 5.0 years using three-dimensional stereophotogrammetric images. Materials and Methods Three-dimensional facial images of healthy children at 1.5, 2.0, 2.5, 3.0, 4.0 and 5.0 years of age were collected and positioned based on a reference frame. A general face template was used to extract the face and its separate regions from the full stereophotogrammetric image. Furthermore, this template was used to create a uniform distributed mesh, which could be directly compared to other meshes. Average faces were created for each age group and mean growth was determined between consecutive groups for the full face and its separate regions. Finally, the results were tested for intra- and inter-operator performance. Results The highest growth velocity was present in the first period between 1.5 and 2.0 years of age with an average of 1.50 mm (±0.54 mm) per six months. After 2.0 years, facial growth velocity declined to only a third at the age of 5.0 years. Intra- and inter-operator variability was small and not significant. Conclusions The results show that this technique can be used for objective clinical evaluation of facial growth. Example normative facial averages and the corresponding facial growth between the age 1.5 and 5.0 years are shown. Clinical Relevance This technique can be used to collect and process facial data for objective clinical evaluation of facial growth in the individual patient. Furthermore, these data can be used as normative data in future comparative studies

Soft- and hard-tissue facial anthropometry in three dimensions: what’s new

In the last few years, technology has provided new instruments for the three-dimensional analysis of human facial morphology. Currently, quantitative assessments of dimensions, spatial positions and relative proportions of distinctive facial features can be obtained for both soft- and hard- (skeletal and dental) tissues. New mathematical tools allow to fuse digital data obtained from various image analyzers, thus providing quantitative information for anatomical and anthropometric descriptions, medical evaluations (clinical genetics, orthodontics, maxillo-facial and plastic surgery), and forensic medicine

Eye Corners Detection using HAAR Cascade Classifiers in Controlled Environment

Facial landmarks detection is undoubtedly important in many applications in computer vision for example face detection and recognition. This article demonstrated the use of Haar Cascade Classifiers to automatically locate the eye corners. We acquired our 3D face image data by Vectra 3D camera in a controlled environment. We use two data set of 300 eye images to train en and ex cascade classifiers regardless of the left and the right eye. These classifiers were then used to detect and locate the inner (en) and outer (ex) eye landmarks. To train HAAR cascade classifier we usually use huge amounts of data. But in this study, about 300 positive images used to train each classifier. Due to this we observed quite an amount of false positive detection. We developed a simple algorithm to predict the eye corners by first eliminate the false detection and geometrically modeled the eye. Our classifiers able to detect and locate en on 53 out of 60 test images and the ability to detect ex in 59 out of 60 test images. In craniofacial anthropometry, it is very important to locate the facial landmarks as per the standard definition of the landmarks. Our results demonstrated accurate detection of ex and en facial landmarks as per standard definition. In conclusion, our trained enHaar and exHaar cascade classifiers are able to automatically detect the en and ex craniofacial landmarks in a controlled environment

Pattern recognition to detect fetal alchohol syndrome using stereo facial images

Fetal alcohol syndrome (FAS) is a condition which is caused by excessive consumption of alcohol by the mother during pregnancy. A FAS diagnosis depends on the presence of growth retardation, central nervous system and neurodevelopment abnormalities together with facial malformations. The main facial features which best distinguish children with and without FAS are smooth philtrum, thin upper lip and short palpebral fissures. Diagnosis of the facial phenotype associated with FAS can be done using methods such as direct facial anthropometry and photogrammetry. The project described here used information obtained from stereo facial images and applied facial shape analysis and pattern recognition to distinguish between children with FAS and control children. Other researches have reported on identifying FAS through the classification of 2D landmark coordinates and 3D landmark information in the form of Procrustes residuals. This project built on this previous work with the use of 3D information combined with texture as features for facial classification. Stereo facial images of children were used to obtain the 3D coordinates of those facial landmarks which play a role in defining the FAS facial phenotype. Two datasets were used: the first consisted of facial images of 34 children whose facial shapes had previously been analysed with respect to FAS. The second dataset consisted of a new set of images from 40 subjects. Elastic bunch graph matching was used on the frontal facial images of the study populaiii tion to obtain texture information, in the form of jets, around selected landmarks. Their 2D coordinates were also extracted during the process. Faces were classified using knearest neighbor (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Principal component analysis was used for dimensionality reduction while classification accuracy was assessed using leave-one-out cross-validation. For dataset 1, using 2D coordinates together with texture information as features during classification produced a best classification accuracy of 72.7% with kNN, 75.8% with LDA and 78.8% with SVM. When the 2D coordinates were replaced by Procrustes residuals (which encode 3D facial shape information), the best classification accuracies were 69.7% with kNN, 81.8% with LDA and 78.6% with SVM. LDA produced the most consistent classification results. The classification accuracies for dataset 2 were lower than for dataset 1. The different conditions during data collection and the possible differences in the ethnic composition of the datasets were identified as likely causes for this decrease in classification accuracy

THREE-DIMENSIONAL FACIAL ANTHROPOMETRY

The use of 3D surface technology is progressively increasing in health clinics and research centers. Methods of capturing 3D facial surface may obtain more imaging information providing a reliable and fast analysis. Stereophotogrammetry is a promising method of soft-tissue evaluation that allows reliable analysis of craniofacial deformities, providing fundamental parameters to plan and evaluate dental treatments and maxillofacial surgery, so improving the multi-disciplinary and multi-species studies of genotype\u2013phenotype correlations through simple and precise measurements. In the current study, photogrammetry/stereophotogrammetry systems were used to evaluate soft-tissue facial morphology and dental casts. Three-dimensional images were collected and rebuilt in 3D, using software for rendering images to establish, analyze and compare morphology features of craniofacial structures, and to assess the usage and limitations of these devices. The use and investigation of this system were divided in 4 studies: 1) A photographic system for the three-dimensional study of facial morphology; 2) Accuracy and reproducibility of a 3D stereophotogrammetry imaging system; 3) Digital dental cast placement in 3-dimensional, full-face reconstruction: A technical evaluation and 4) Unilateral Cleft Lip and Palate (UCLP): a 3D evaluation. The current studies found the used 3D image systems both accurate and repeatable. The 3D devices and the methods analyzed in these studies could therefore be usefully used for clinical analysis in maxillofacial, plastic and esthetic surgery, as well as in all dental fields. The 3D stereophotogrammetric systems have several advantages over direct anthropometry and gradually are becoming into more accessible cost, replacing classical methods to quantify surface topography

Estado actual de la técnica y cuestiones perdurables en la recogida de datos antropométricos

The study of human body size and shape has been a topic of research for a very long time. In the past, anthropometry used traditional measuring techniques to record the dimensions of the human body and reported variance in body dimensions as a function of mean and standard deviation. Nowadays, the study of human body dimensions can be carried out more efficiently using three-dimensional body scanners, which can provide large amounts of anthropometric data more quickly than traditional techniques can. This paper presents a description of the broad range of issues related to the collection of anthropometric data using three-dimensional body scanners, including the different types of technologies available and their implications, the standard scanning process needed for effective data collection, and the possible sources of measurement errors that might affect the reliability and validity of the data collected.El estudio del tamaño y la forma del cuerpo humano ha sido un tema de investigación durante un tiempo muy largo. En el pasado, la antropometría utilizó técnicas de medición tradicionales para registrar las dimensiones del cuerpo humano y reportó la variación en las dimensiones del cuerpo en función de la media y la desviación estándar. Hoy en día, el estudio de las dimensiones del cuerpo humano se puede llevar a cabo utilizando maneras más eficientes, como los escáneres tridimensionales del cuerpo, que pueden proporcionar grandes cantidades de datos antropométricos más rápidamente que las técnicas tradicionales. En este trabajo se presenta una descripción de la amplia gama de temas relacionados con la recogida de datos antropométricos utilizando escáneres tridimensionales del cuerpo, incluyendo los diferentes tipos de tecnologías disponibles y sus implicaciones, el proceso de digitalización estándar necesario para la captura efectiva de datos, y las posibles fuentes de los errores de medición que podrán afectar la fiabilidad y validez de los datos recogidos.This work is financed by FEDER funds through the Competitive Factors Operational Program (COMPETE) POCI-01-0145-FEDER-007043 and POCI-01-0145FEDER-007136 and by national funds through FCT – the Portuguese Foundation for Science and Technology, under the projects UID/CEC/00319/2013 and UID/CTM/00264 respectively

3-D surface modelling of the human body and 3-D surface anthropometry

This thesis investigates three-dimensional (3-D) surface modelling of the human body and 3-D surface anthropometry. These are two separate, but closely related, areas. 3-D surface modelling is an essential technology for representing and describing the surface shape of an object on a computer. 3-D surface modelling of the human body has wide applications in engineering design, work space simulation, the clothing industry, medicine, biomechanics and animation. These applications require increasingly realistic surface models of the human body. 3-D surface anthropometry is a new interdisciplinary subject. It is defined in this thesis as the art, science, and technology of acquiring, modelling and interrogating 3-D surface data of the human body. [Continues.

Detailing patient specific modelling to aid clinical decision-making

The anatomy of the craniofacial skeleton has been described through the aid of dissection identifying hard and soft tissue structures. Although the macro and microscopic investigation of internal facial tissues have provided invaluable information on constitution of the tissues it is important to inspect and model facial tissues in the living individual. Detailing the form and function of facial tissues will be invaluable in clinical diagnoses and planned corrective surgical interventions such as management of facial palsies and craniofacial disharmony/anomalies. Recent advances in lower-cost, non-invasive imaging and computing power (surface scanning, Cone Beam Computerized Tomography (CBCT) and Magnetic Resonance (MRI)) has enabled the ability to capture and process surface and internal structures to a high resolution. The three-dimensional surface facial capture has enabled characterization of facial features all of which will influence subtleties in facial movement and surgical planning. This chapter will describe the factors that influence facial morphology in terms of gender and age differences, facial movement—surface and underlying structures, modeling based on average structures, orientation of facial muscle fibers, biomechanics of movement—proof of principle and surgical intervention