Facial Expression Recognition using Neural Network for Dyadic Interaction
Kuupäev
2020
Autorid
Ajakirja pealkiri
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Tartu Ülikool
Abstrakt
Computers are machines that don’t share emotions as humans do. With the help of Machine
Learning (ML) and Artificial Intelligence (AI), social robots can become a reality. These robots
are currently capable of interacting with people at a certain level, but not exactly as a person
would do. For them to reach that level, they would need to understand more about how people
interact daily and to learn from the dyadic interaction of two people would be a good option.
Participants’ facial expressions are the main features that can be retrieved from dyadic interaction
and this can be done using a trained Deep Neural Network (DNN) model. The DNN model,
known as the Mini-Xception, is trained in this thesis using a dataset that has been pre-processed
and can then be tested on images. Using a face detector algorithm, the model will be able to
detect a person’s facial expression on the image. After successful image results, the model can
be tested using a different medium. First, the tests are carried out using a webcam, then videos
with more than one participant. Since people react to expressions, their reactions can also be
caused by a context in which, for example, sad news would be the reason for sad emotion. The
results of the tests will, therefore, be used for analysis where a correlation can be constructed
between facial expressions and context.
Kirjeldus
Märksõnad
Machine Learning, Artificial Intelligence, Deep Neural Network, Social Robot, Mini-Xception, face detector algorithm, facial expression recognition, masinõpe, Tehisintellekt, sügav närvivõrk, sotsiaalne robot, näotuvastusalgoritm, näoilme tuvastus