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Objective: Existing patient-reported outcome measures (PROM)s for patients with facial differences lack content validity, as few items address appearance and function issues. The FACE-Q is a new PROM developed to measure outcomes important to patients aged 8-29 years with craniofacial conditions. A process was needed to determine if the FACE-Q content is relevant to patients with head and neck cancer (HNC). Methods: Cognitive interviews with patients with HNC aged 8 to 29 years (n=15) were conducted and feedback from experts in pediatric oncology (n=21) was obtained. Input was sought on all aspects of the FACE-Q content. Results: A total of 1573 codes were developed from patient comments and 234 codes were developed from expert feedback that related to the COSMIN criteria for judging content validity. A total of 12 items were flagged for review from qualitative interviews and 4 comments were coded from expert feedback among the core scales for comprehensibility. Instructions, time frame, and response options were found to be comprehensible and appropriate by almost all patient and expert participants. Participants identified a total of 10 missing items identified across the core scales, while no additional items were identified by experts for the core scales. However, 4 experts identified swallowing/dysphagia as an important item missing from the mouth function scale. Discussion: Content validity of the FACE-Q for patients with HNC was evaluated through cognitive interviews with patients and feedback from pediatric oncology experts. The core scales were answered by all participants and demonstrate overall content validity from feedback offered by both patients and experts. Conclusion: The FACE-Q showed evidence of content validity for its core scales along with limited evidence that the remaining scales covered issues relevant to specific HNC patients. Assessment of the psychometric properties of the new measure is forthcoming as part of an international FACE-Q field-test study. / Thesis / Master of Science (MSc) / The FACE-Q is a patient-reported outcome measure developed to assess outcomes important to patients aged 8-29 years with craniofacial conditions. The current study aimed to determine its content validity for use in patients with head and neck cancer (HNC). Cognitive interviews with patients with HNC aged 8-29 years (n=15) were conducted and feedback from experts in pediatric oncology (n=21) was obtained. A total of 1573 codes from patient comments and 234 codes from expert feedback were developed. A total of 12 items were flagged for review from qualitative interviews along with 4 items from expert feedback among the core scales for comprehensibility. Instructions and response options were found to be comprehensible and appropriate. A total of 10 missing items were identified across the core scales by patient participants while experts identified 1 missing item. The FACE-Q evidenced content validity for core scales along with limited evidence for remaining scales.

Yes / Today, computer based face recognition is a mature and reliable mechanism which is being practically utilised for many access control scenarios. As such, face recognition or authentication is predominantly performed using ‘perfect’ data of full frontal facial images. Though that may be the case, in reality, there are numerous situations where full frontal faces may not be available — the imperfect face images that often come from CCTV cameras do demonstrate the case in point. Hence, the problem of computer based face recognition using partial facial data as probes is still largely an unexplored area of research. Given that humans and computers perform face recognition and authentication inherently differently, it must be interesting as well as intriguing to understand how a computer favours various parts of the face when presented to the challenges of face recognition. In this work, we explore the question that surrounds the idea of face recognition using partial facial data. We explore it by applying novel experiments to test the performance of machine learning using partial faces and other manipulations on face images such as rotation and zooming, which we use as training and recognition cues. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the cheek. We also study the effect of face recognition subject to facial rotation as well as the effect of recognition subject to zooming out of the facial images. Our experiments are based on using the state of the art convolutional neural network based architecture along with the pre-trained VGG-Face model through which we extract features for machine learning. We then use two classifiers namely the cosine similarity and the linear support vector machines to test the recognition rates. We ran our experiments on two publicly available datasets namely, the controlled Brazilian FEI and the uncontrolled LFW dataset. Our results show that individual parts of the face such as the eyes, nose and the cheeks have low recognition rates though the rate of recognition quickly goes up when individual parts of the face in combined form are presented as probes.

Yes / Accurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning face recognition models to evaluate their capacity for discrimination between sibling faces using various similarity indices. The specific models examined for this purpose are FaceNet, VGGFace, VGG16, and VGG19. For each pair of images provided, the embeddings have been calculated using the chosen deep learning model. Five standard similarity measures, namely, cosine similarity, Euclidean distance, structured similarity, Manhattan distance, and Minkowski distance, are used to classify images looking for their identity on the threshold defined for each of the similarity measures. The accuracy, precision, and misclassification rate of each model are calculated using standard confusion matrices. Four different experimental datasets for full-frontal-face, eyes, nose, and forehead of sibling pairs are constructed using publicly available HQf subset of the SiblingDB database. The experimental results show that the accuracy of the chosen deep learning models to distinguish siblings based on the full-frontal-face and cropped face areas vary based on the face area compared. It is observed that VGGFace is best while comparing the full-frontal-face and eyes—the accuracy of classification being with more than 95% in this case. However, its accuracy degrades significantly when the noses are compared, while FaceNet provides the best result for classification based on the nose. Similarly, VGG16 and VGG19 are not the best models for classification using the eyes, but these models provide favorable results when foreheads are compared.

Yes / It has been proposed that mentalising involves retrodicting as well as predicting behaviour, by inferring previous mental states of a target. This study investigated whether retrodiction is impaired in individuals with Autism Spectrum Disorders (ASD). Participants watched videos of real people reacting to the researcher behaving in one of four possible ways. Their task was to decide which of these four “scenarios” each person responded to. Participants’ eye movements were recorded. Participants with ASD were poorer than comparison participants at identifying the scenario to which people in the videos were responding. There were no group differences in time spent looking at the eyes or mouth. The findings imply those with ASD are impaired in using mentalising skills for retrodiction.

The purpose of this dissertation is to examine the occurrence of facial fractures in automotive collisions and to determine the tolerance of the facial bones to blunt impact. The effects of restraint use, impact severity and impact direction on facial fractures were evaluated using the NASS-CDS database. The association between brain injury and facial fractures was also examined. The tolerance of the frontal bone, nasal bone, maxilla and mandible was determined using the flat surface of a cylindrical impactor. The influence of anthropometric measures and geometrical descriptors on the tolerance of the facial bones is also presented. The force-displacement response of each impacted region was also determined and response corridors were created. These corridors were used to evaluate the biofidelity of the FOCUS headform under the same impact conditions. Mathematical models were also created to predict the force and displacement resulting from facial impact. The data contained in this dissertation can be used to determine the risk of facial fracture as a function of impact force and evaluate the biofidelity of models simulating facial impact. / Ph. D.

This thesis is out from the relationship between imagination and memory. Imagination is always from memory. After expressing my imagination through pencil, things that are invinted are actually familier to the past. Based on what I have done about my thesis, the invention of using mirror image as a dimension system come up. / Master of Architecture