Biological tissues exhibit complicated motions, like stretching and sliding in-plane, that can have limited impact on the shape of the tissue. These may make identifying constitutive behaviours more difficult. We provide a method for embedding sparse texture-based features into mathematical models, and track an induced deformation in porcine skin using a five camera stereoscope. However we demonstrate that, although these features improve the recovery of the surface motion, sparse features are weak constraints on the surrounding tissues and allow error to accumulate. As such we question what can be inferred from sparse tracking methods, and suggest that augmentation with dense tracking may be necessary for high precision tracking of biological tissues.