Deformable modeling of facial soft tissues have found use in application domains such as human-machine interaction for facial expression recognition. More recently, such modeling techniques have been used for tasks like age estimation and person identification. This dissertation is focused on development of novel image analysis algorithms to follow facial strain patterns observed through video recording of faces in expressions. Specifically, we use the strain pattern extracted from non-rigid facial motion as a simplified and adequate way to characterize the underlying material properties of facial soft tissues. Such an approach has several unique features. Strain pattern instead of the image intensity is used as a classification feature. Strain is related to biomechanical properties of facial tissues that are distinct for each individual. Strain pattern is less sensitive to illumination differences (between enrolled and query sequences) and face camouflage because the strain pattern of a face remains stable as long as reliable facial deformations are captured. A finite element modeling based method enforces regularization which mitigates issues (such as temporal matching and noise sensitivity) related to automatic motion estimation. Therefore, the computational strategy is accurate and robust. Images or videos of facial deformations are acquired with video camera and without special imaging equipment. Experiments using range images on a dataset consisting of 50 subjects provide the necessary proof of concept that strain maps indeed have a discriminative value. On a video dataset containing 60 subjects undergoing a particular facial expression, experimental results using the computational strategy presented in this work emphasize the discriminatory and stability properties of strain maps across adverse data conditions (shadow lighting and face camouflage). Such properties make it a promising feature for image analysis tasks that can benefit from such auxiliary information about the human face. Strain maps add a new dimension in our abilities to characterize a human face. It also fosters newer ways to capture facial dynamics from video which, if exploited efficiently, can lead to an improved performance in tasks involving the human face. In a subsequent effort, we model the material constants (Young's modulus) of the skin in sub-regions of the face from the motion observed in multiple facial expressions. On a public database consisting of 40 subjects undergoing some set of facial motions, we present an expression invariant strategy to matching faces using the Young's modulus of the skin. Such an efficient way of describing underlying material properties from the displacements observed in video has an important application in deformable modeling of physical objects which are usually gauged by their simplicity and adequacy. The contributions through this work will have an impact on the broader vision community because of its highly novel approaches to the long-standing problem of motion analysis of elastic objects. In addition, the value is the cross disciplinary nature and its focus on applying image analysis algorithms to the rather difficult and important problem of material property characterization of facial soft tissues and their applications. We believe this research provides a special opportunity for the utilization of video processing to enhance our abilities to make unique discoveries through the facial dynamics inherent in video.

The aerospace industry endeavours to improve modern aircraft capabilities in efficiency, endurance, and comfort. One means of achieving these goals is through new enhancements in aerodynamics. Increased wing aspect ratio is an example of further improving efficiency. However, this comes with new challenges including possibly adverse aero-elastic and aero-servo-elastic (ASE) phenomena. New computational methods and tools are emerging and there is a need for experimental data for validation. University of Victoria’s Centre for Aerospace Research (UVic CfAR) set out to design a 20kg ASE demonstrator using a remotely piloted aircraft (RPA). This aircraft was designed with the intent of exploring coupling between aero-elastic modes including coupling between the short period aerodynamic mode and the first out-of-plane elastic mode of the wing. This thesis discuses the implementation of instrumentation designed and integrated into the ASE RPA demonstrator to monitor the deformation of the elastic wing in-flight. A strain based measurement technique was selected for integration into the ASE aircraft. This choice was made for several reasons including its reliability regardless of outdoor lighting, relatively lightweight processing requirements for real time applications, and suitable sampling bandwidth. To compute the wing deformation from strain, a method, sometimes referred to as strain pattern analysis (SPA), utilizing linear combinations of reference modal shapes fit against the measured strain, was used. Although this method is not new, to the author’s knowledge, it is the first practical application to a reduced scale RPA demonstrator. The deformation measurement system was validated against a series of distributed static load tests on the ground. Distributed load cases along the wing demonstrated good out-of-plane measurement performance. A case where only load is applied near the root of the wing resulted in the largest error in part as the mode shapes generated are less suited to approximate the resulting shape. In general errors in out-of-plane displacement at the end of the flexible wing portion can be expected to be less than 5%. The displacement at the tip of the wing can be as great as 11% for the left wing whereas the right wing is 4.7%. This suggest an asymmetry between the left and right wings requiring specifically tuned FE models for each to achieve best results. Twist angles presented in tests were relatively small for accurate comparison against the reference measurement, which was relatively noisy. Generally, the deformation measurement by SPA technique followed the same twist behaviours as the reference. A twist case, unlikely to be seen in flight, provided some insight into twist measurement robustness. The work presented is merely a small step forward with many opportunities for further research. There is room for improvement of the FE model used to generate the mode shapes in the strain pattern analysis. Initial efforts focused on the flexible spar portion of the wing. With more work improvements could be achieved for the estimation of the rigid wing. Additionally, there was some asymmetry between each wing semi-span, and with some focus on the left wing its results could be improved to at least match that of the right wing. A real-time implementation was not completed and would be particularly interesting for use as feedback for flight control. Study of load alleviation techniques may benefit. Another topic of study is the combination of this method with other measurements, such as accelerometers, to provide improved performance state estimation through sensor fusion. / Graduate