  This paper proposes to expand the visual understanding capacity of computers
by helping it recognize human sign language more efficiently. This is carried
out through recognition of facial expressions, which accompany the hand signs
used in this language. This paper specially focuses on the popular Brazilian
sign language (LIBRAS). While classifying different hand signs into their
respective word meanings has already seen much literature dedicated to it, the
emotions or intention with which the words are expressed haven't primarily been
taken into consideration. As from our normal human experience, words expressed
with different emotions or mood can have completely different meanings attached
to it. Lending computers the ability of classifying these facial expressions,
can help add another level of deep understanding of what the deaf person
exactly wants to communicate. The proposed idea is implemented through a deep
neural network having a customized architecture. This helps learning specific
patterns in individual expressions much better as compared to a generic
approach. With an overall accuracy of 98.04%, the implemented deep network
performs excellently well and thus is fit to be used in any given practical
scenario.
