Even though APIs and facial processing are new areas of technology, there are already an
abundance of cloud-based APIs that provide facial processing services to companies,
researchers, and government agencies. Google, Microsoft, IBM, and Amazon are some of
the major companies that offer these services; however, there are dozens of other
companies out there, from startups to established multinationals, that offer the same
services. Many of the lesser known companies may be cheaper and provide more optimal
solutions, i.e. better measured performance, however they lack the name recognition of
the software giants. Because of the dominance of these software giants, i.e. Microsoft,
Google, IBM, and Amazon, face processing has received negative coverage in recent
news due to lack of performance, gender bias, race bias, and privacy concerns [60, 61,
62]. In some cases, the lesser known entities may have developed algorithms that
outperform the giant’s, but their collective voices are muted due to the press coverage
around the giants. For this reason, I developed a meta-API which calls several face
processing APIs, aggregates the outputs, and delivers the outputs in a user configured
manner. An end user could use this system to evaluate several solutions against the same
set of inputs, performing deep evaluations on against a set of solutions, which currently
does not exist. An end user could also aggregate the results based on the solution that
performs the best on a given output to mitigate against algorithm bias or, simply, to
generate the most accurate result.