With the increasing awareness of personal and national safety, security and surveillance systems are commonly encountered in our everyday lives. Computer vision technologies such as modeling and recognition are the key components of current surveillance systems. Over the last few years, many studies have been conducted for facial recognition using data acquired by imaging systems such as video and laser scanning. With the advent of low-cost digital cameras, photogrammetry has emerged as a cost-effective and straightforward technique to accurately acquire three-dimensional facial measurements. For identification and verification purposes, generated facial models should undergo surface registration and matching procedures. In addition to facial recognition, the surface matching can be used for identifying resulting discrepancies from different facial expressions. The presented research introduces a novel approach for using low-cost digital cameras and surface matching to generate and recognize corresponding facial models. Preliminary experimental results showed that the proposed algorithms could successfully match and detect discrepancies between two facial models. The performance, advantages, and limitations of this preliminary study will be discussed, along with recommendations for future research.