Grammatical facial expression recognition using customized deep neural network architecture
This addresses the need for computers to better interpret deaf communication by adding emotional context to sign language recognition, though it is incremental as it builds on existing hand sign classification work.
The paper tackles the problem of recognizing facial expressions in Brazilian sign language (LIBRAS) to enhance computer understanding of sign language by capturing emotional intent, achieving an overall accuracy of 98.04% with a customized deep neural network.
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.