  Gender classification algorithms have important applications in many domains
today such as demographic research, law enforcement, as well as human-computer
interaction. Recent research showed that algorithms trained on biased benchmark
databases could result in algorithmic bias. However, to date, little research
has been carried out on gender classification algorithms' bias towards gender
minorities subgroups, such as the LGBTQ and the non-binary population, who have
distinct characteristics in gender expression. In this paper, we began by
conducting surveys on existing benchmark databases for facial recognition and
gender classification tasks. We discovered that the current benchmark databases
lack representation of gender minority subgroups. We worked on extending the
current binary gender classifier to include a non-binary gender class. We did
that by assembling two new facial image databases: 1) a racially balanced
inclusive database with a subset of LGBTQ population 2) an inclusive-gender
database that consists of people with non-binary gender. We worked to increase
classification accuracy and mitigate algorithmic biases on our baseline model
trained on the augmented benchmark database. Our ensemble model has achieved an
overall accuracy score of 90.39%, which is a 38.72% increase from the baseline
binary gender classifier trained on Adience. While this is an initial attempt
towards mitigating bias in gender classification, more work is needed in
modeling gender as a continuum by assembling more inclusive databases.
