alarm rate (reporting of incorrect matches) and a high true detection rate (reporting of correct matches).
Simultaneously achieving a low false alarm rate and high true detection rate is well known in the statistics and pattern recognition scientific literatures to be challenging, although for many tasks not insurmountable. A given false alarm rate and true detection rate may even produce acceptable performance for a particular database size but still not scale up effectively to larger databases. For instance, if a database grows by a factor of 100, for a given false alarm rate the number of incorrect matches reported will also be expected to grow by a factor of 100. This may simply be too many potential leads to follow up on. Thus, as a rule of thumb, the false alarm rate often must get better (lower) as the database size increases, while at the same time maintaining the true detection rate.
Early ballistic image “databases” consisted of photographs of bullets and shell casings hanging on the wall. These photographs were taken with a camera attached to a forensic microscope. For unsolved cases these photographs served as reminders in the event that an examiner encountered other evidence that could possibly be tied to these cases. Ballistic image database systems, such as NIBIN, can be viewed as a means of automating this manual process of hanging photos on the wall, enabling investigators to potentially tie cases together based on images of a larger number of bullets and shell casings than can be considered by manual inspection. These systems are now routinely used to handle much larger databases of ballistic images than one could hang on a wall, and in several law enforcement jurisdictions have been effective for finding “cold hits” or links between cases that were not otherwise known.
One can thus view NIBIN as an illustration of the potential that an automated image database search has to increase the capacity to tie cases together in comparison with the manual examination of images (or evidence itself). However, as detailed throughout this report, there is a finite limit on the extent to which such a database can be scaled up and still prove useful. This is both an empirical fact for the particular technologies used by the NIBIN system and a question of both theory and experimentation for other imaging technologies and other pattern recognition techniques. In this chapter we briefly review some of the relevant technologies and techniques and offer suggestions for improving the system.
The goal of visual pattern recognition methods is to find possible matches between images. Pattern recognition methods can be used as part of either a verification or a search task. As discussed above, the former involves validating a particular hypothesis, in this case assessing a potential