CGV: Small: Digital Forensic Facial Reconstruction from Incomplete Datasets

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In forensic investigations, the skeletal evidence analysis and facial reconstruction play important roles in identification of the decedent. In current practice, effective skull processing and facial reconstruction are accomplished manually. This project studies 3D geometric analysis and modeling algorithms to automate and augment fragmented/incomplete skull restoration and craniofacial reconstruction and advocates a potential evolution of manual processing to a digital platform for better efficiency, robustness, and objectivity. Two key challenging problems to solve in these forensic tasks are geometric restoration (from small fragmented pieces) and geometric shape synthesis (with complex geometric and semantic constraints). This project studies effective 3D shape matching and transformation techniques to tackle these problems. Geometric restoration is solved through reliable partial matching and symmetry guidance; geometric synthesis is explored via heterogeneous volumetric deformation that enforces given geometric constraints on non-uniform interior layers. The new geometric algorithms can benefit incomplete data modeling and analysis in many computer graphics and vision tasks. In this project, computer scientists are collaborating with forensic specialists to build a digital computational platform and evaluate the application of these new geometric algorithms in forensic skull processing and craniofacial reconstruction. This project facilitates incomplete data analysis and reconstruction in forensic law enforcement, archaeology, biological anthropology, and craniofacial orthopedics. The research and education are integrated by taking research advances into existing and future courses, involving RET/REU/K-12 students in geometric modeling research and graphics/visualization system development; and attracting K-12 and under-representative students into STEM education.