Structural health monitoring (SHM) describes a decision-making framework that is fundamentally guided by state change detection of structural systems. This framework typically relies on the use of continuous or semi-continuous monitoring of measured response to quantify this state change in structural system behavior, which is often related to the initiation of some form of damage. Measurement approaches used for traditional SHM are numerous, but most are limited to either describing localized or global phenomena, making it challenging to characterize operational structural systems which exhibit both. In addition to these limitations in sensing, SHM has also suffered from the inherent robustness inherent to most full-scale structural systems, making it challenging to identify local damage. These challenges highlight the opportunity for alternative strategies for SHM, strategies that are able to provide data suitable to translate into rich information. This paper describes preliminary results from a refined structural identification (St-ID) approach using fullfield measurements derived from high-speed 3D Digital Image Correlation (HSDIC) to characterize uncertain parameters (i.e. boundary and constitutive properties) of a laboratory scale structural component. The St-ID approach builds from prior work by supplementing full-field deflection and strain response with vibration response derived from HSDIC. Inclusion of the modal characteristics within a hybrid-genetic algorithm optimization scheme allowed for simultaneous integration of mechanical and modal response, thus enabling a more robust St-ID strategy than could be achieved with traditional sensing techniques. The use of full-field data is shown to provide a more comprehensive representation of the global and local behavior, which in turn increases the robustness of the St-Id framework. This work serves as the foundation for a new paradigm in SHM that emphasizes characterizing structural performance using a smaller number, but richer set of measurements.