Three – Dimensional Developments for the Future
Three- dimensional (3D) measurements have been possible since the concept of what we know as Pythagoras’ theorem, documented around 500 BC, although it is known that the Babylonians were aware of the concepts 1000 years previously. Perhaps it was Pythagoras that proved the theory and became a “household” name. As basic trigonometry can be used to define and quantify shapes and volumes, it has been used by surveyors, architects, and designers to create objects and buildings. It is easy to construct objects from plans, but it has been harder to achieve the reverse, that is, to develop 3D plans from established structures.
In 1859, Aimé Laussedat was one of the first to plot Paris by plane-table photogrammetry. Since then, the technique of photogrammetry has been refined, supported by technologic advances. Traditional assessment is to break down the dimensions into three planes (x–y, frontal elevation; y–z, side elevation; and x–z, plan view). This method tends to oversimplify an object as there is a considerable amount of information other than these projections. The development of image capture and analyses has been restricted due to technologic restrictions in both hardware and software. In recent years, the various techniques of capturing three dimensions have improved, in association with reduced costs of equipment and software, which makes the techniques affordable and usable by a wide variety of disciplines.
There is no doubt that 3D imaging is having a significant impact on everyday activities through the medical, retail, and entertainment industries. There has been no lack of ideas in the applications and implementation of 3D imaging. However, researchers have been waiting for significant improvements in the development of easy-to-use, low-cost, high-resolution, non-life-threatening acquisition systems, suitable algorithms, and significant computer programming power. 3D imaging can generate a large volume of data that can take many hours to analyze, although processing times can be significantly reduced using multiple core processors and computer clusters. Nevertheless, these new imaging systems enable the option of recording and quantifying facial data and, with the advancement of computing technology, there is no doubt that processing times will decrease, allowing their routine use.
The drive over the next 10 years will be to develop a suite of commercially available software programs that will automatically register, landmark, compare, evaluate, and report surface changes using all the acquired facial data.
Automatic dynamic facial identification software has enormous potential not only in the security industry, but also in healthcare. Since it is possible to identify a single individual with a high degree of sensitivity and specificity, it will be possible to identify groups of individuals with similar facial morphologies and facial characteristics. Already it is possible to identify facial dysmorphology in static images,1 and once large datasets have been accrued, dynamic images will facilitate the automatic diagnosis of various types of facial morphology, the best approach to intervention leading t/>