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

An investigation into the use of stereophotogrammetry for the analysis of craniofacial dysmorphology in schizophrenia

Studies of craniofacial dysmorphology in schizophrenia, carried out since the 1960s, have reported minor physical anomalies in those with schizophrenia, prominently in the craniofacial region. Indirect methods, most notably 3D laser imaging, have been used previously for investigating craniofacial dysmorphology in schizophrenia. This project aimed to investigate the ability of a stereophotogrammetry system to detect craniofacial dysmorphology in individuals diagnosed with schizophrenia. Furthermore, observed dysmorphology was characterised and compared with that found in previous studies. Three-dimensional craniofacial landmark coordinates were obtained from images collected using a bespoke design stereophotogrammetry system. The system includes a camera rig and a calibration rig. On the camera rig is mounted three digital single-lens reflex cameras hardwired to a trigger for simultaneous image capture. The calibration rig consists of a frame with strategically positioned retro-reflective calibration markers of known 3D orientation. The precision and reliability of the stereophotogrammetry system was tested using a human subject. Measurements were taken using the system and directly using callipers by two operators on two separate occasions. Intra- and inter-operator precision and inter-modality reliability were calculated and scored. All intra- and inter-operator precision scores were at least below a 7% error, and considered "good". Inter -modality reliability scores had at least a "good" score in 72% of all measurements. Excluding one soft landmark and one landmark with small measurement value, all inter-modality reliability scores were at least "good". The study cohort consisted of 17 African (8 control, 9 schizophrenia) and 13 Caucasian ( 8 control, 5 schizophrenia) males. A set of 18 landmarks focused about the eyes, nose, mouth and chin was identified for each subject and collated in 3D coordinate space. Geometric morphometric analysis - particularly generalised Procrustes analysis and principal component analysis - was carried out on these landmark sets. Discriminant Function Analysis was applied to identify discriminating features in the data set, and classification techniques, aided by feature selection, were applied to separate affected and control subjects. In the African cohort, the results showed wider inward slanting (cat-like) eyes, a wider upturned nose and narrower downturned mouth. In the Caucasian cohort, narrower and wide set eyes, a narrower downturned nose with anteriorly displaced alare, a wider downturned mouth and posteriorly set chin were shown. The Caucasian cohort demonstrates similar dysmorphology as described in the literature. Published data for the African cohort is lacking. The nearest mean and k- nearest neighbour classifiers had the highest accuracy in the African and Caucasian groups respectively, with 71% and 77% correct classification. The efficacy of the stereophotogrammetry system introduced in this study has been shown, with craniofacial dysmorphology in schizophrenia successfully detected. Further studies with larger cohorts are recommended to attempt improved classification accuracy, but a platform now exists to pursue dysmorphology studies in other psychoses, such as bipolar disorder

Maschinelles Sehen als Hilfsmittel in der Differentialdiagnostik des Cushing-Syndroms

Objective: Cushing's syndrome is a rare disease characterized by clinical features that show morphological similarity with the metabolic syndrome. Distinguishing these diseases is a challenge in clinical practice. We have previously shown that computer vision technology can be a potentially useful diagnostic tool in Cushing's syndrome. In this follow-up study, we addressed the described problem by increasing the sample size and including controls matched by age and body-mass-index. Methods: 82 patients (22 male, 60 female) and 98 control subjects (32 male, 66 female) matched by age, gender and body-mass-index were included. The control group consisted of patients with initially suspected, but biochemically excluded Cushing's syndrome. Standardized frontal and profile facial digital photographs were acquired. The images were analyzed using specialized computer vision and classification software. A grid of nodes was semi-automatically placed on disease-relevant facial structures for analysis of texture and geometry. Classification accuracy was calculated using a leave-one-one cross-validation procedure with a maximum likelihood classifier. Results: The overall correct classification rates were 10/22 (45.5%) for male patients and 26/32 (81.3%) for male controls, and 34/60 (56.7%) for female patients and 43/66 (65.2%) for female controls. In subgroup analyses, correct classification rates were higher for iatrogenic than for endogenous Cushing's syndrome. Conclusion: Regarding the advanced problem of detecting Cushing's syndrome within a study sample matched by body-mass-index, we found moderate classification accuracy by facial image analysis. Classification accuracy is most likely higher in a sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm

Maschinelles Sehen als Hilfsmittel in der Differentialdiagnostik des Cushing-Syndroms

Objective: Cushing's syndrome is a rare disease characterized by clinical features that show morphological similarity with the metabolic syndrome. Distinguishing these diseases is a challenge in clinical practice. We have previously shown that computer vision technology can be a potentially useful diagnostic tool in Cushing's syndrome. In this follow-up study, we addressed the described problem by increasing the sample size and including controls matched by age and body-mass-index. Methods: 82 patients (22 male, 60 female) and 98 control subjects (32 male, 66 female) matched by age, gender and body-mass-index were included. The control group consisted of patients with initially suspected, but biochemically excluded Cushing's syndrome. Standardized frontal and profile facial digital photographs were acquired. The images were analyzed using specialized computer vision and classification software. A grid of nodes was semi-automatically placed on disease-relevant facial structures for analysis of texture and geometry. Classification accuracy was calculated using a leave-one-one cross-validation procedure with a maximum likelihood classifier. Results: The overall correct classification rates were 10/22 (45.5%) for male patients and 26/32 (81.3%) for male controls, and 34/60 (56.7%) for female patients and 43/66 (65.2%) for female controls. In subgroup analyses, correct classification rates were higher for iatrogenic than for endogenous Cushing's syndrome. Conclusion: Regarding the advanced problem of detecting Cushing's syndrome within a study sample matched by body-mass-index, we found moderate classification accuracy by facial image analysis. Classification accuracy is most likely higher in a sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm

3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies

The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17–0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation

Locating landmarks using templates

Abstract: This thesis is devoted to automatic location of landmarks (mouth and eyes) in images of faces using templates. There is an unsatisfactory experience with existing software because of its high sensitivity to small rotations of the face. The weighted correlation coeficient as a similarity measure between the template and the image turns out to outperform the classical correlation. It is presented how to choose the weights to increase the discrimination of the parts of the face which correspond to the template from those which do not. Optimization without constraints tends to degenerate and to obtain a robust version we bound the in uence of single pixels. In a similar way the template can be optimized to improve the discrimination further. The results are compared for different initial choices of weights and their robustness to different size or rotation of the face is examined. The method does not use any special properties of the mouth or eyes and can be classified as a robust nonparametric disrimination technique

3D Face Modelling, Analysis and Synthesis

Human faces have always been of a special interest to researchers in the computer vision and graphics areas. There has been an explosion in the number of studies around accurately modelling, analysing and synthesising realistic faces for various applications. The importance of human faces emerges from the fact that they are invaluable means of effective communication, recognition, behaviour analysis, conveying emotions, etc. Therefore, addressing the automatic visual perception of human faces efficiently could open up many influential applications in various domains, e.g. virtual/augmented reality, computer-aided surgeries, security and surveillance, entertainment, and many more. However, the vast variability associated with the geometry and appearance of human faces captured in unconstrained videos and images renders their automatic analysis and understanding very challenging even today. The primary objective of this thesis is to develop novel methodologies of 3D computer vision for human faces that go beyond the state of the art and achieve unprecedented quality and robustness. In more detail, this thesis advances the state of the art in 3D facial shape reconstruction and tracking, fine-grained 3D facial motion estimation, expression recognition and facial synthesis with the aid of 3D face modelling. We give a special attention to the case where the input comes from monocular imagery data captured under uncontrolled settings, a.k.a. \textit{in-the-wild} data. This kind of data are available in abundance nowadays on the internet. Analysing these data pushes the boundaries of currently available computer vision algorithms and opens up many new crucial applications in the industry. We define the four targeted vision problems (3D facial reconstruction $\&$ tracking, fine-grained 3D facial motion estimation, expression recognition, facial synthesis) in this thesis as the four 3D-based essential systems for the automatic facial behaviour understanding and show how they rely on each other. Finally, to aid the research conducted in this thesis, we collect and annotate a large-scale videos dataset of monocular facial performances. All of our proposed methods demonstarte very promising quantitative and qualitative results when compared to the state-of-the-art methods

Neurological and Mental Disorders

Mental disorders can result from disruption of neuronal circuitry, damage to the neuronal and non-neuronal cells, altered circuitry in the different regions of the brain and any changes in the permeability of the blood brain barrier. Early identification of these impairments through investigative means could help to improve the outcome for many brain and behaviour disease states.The chapters in this book describe how these abnormalities can lead to neurological and mental diseases such as ADHD (Attention Deficit Hyperactivity Disorder), anxiety disorders, Alzheimer’s disease and personality and eating disorders. Psycho-social traumas, especially during childhood, increase the incidence of amnesia and transient global amnesia, leading to the temporary inability to create new memories.Early detection of these disorders could benefit many complex diseases such as schizophrenia and depression