Real Time Facial Expression Recognition for
Nonverbal Communication
Md. Sazzad Hossain1 and Mohammad Abu Yousuf2
1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh
2Institute of Information Technology, Jahangirnagar University, Bangladesh
Abstract: This paper represents a system which can
understand and react appropriately to human facial expression for nonverbal
communications. The considerable events of this system are detection of human
emotions, eye blinking, head nodding and shaking. The key step in the system is to appropriately recognize a human face
with acceptable labels. This system uses currently developed OpenCV Haar
Feature-based Cascade Classifier for face detection because it can detect faces
to any angle. Our system can recognize emotion which is divided into several
phases: segmentation of facial regions, extraction of facial features and
classification of features into emotions. The first phase of processing is to identify
facial regions from real time
video. The second phase of processing identifies features which can be used as
classifiers to recognize facial expressions. Finally, an artificial neural
network is used in order to classify the identified features into five basic
emotions. It can also detect eye blinking accurately. It works for the active
scene where the eye moves freely and the head and the camera moves
independently in all directions of the face. Finally, this system can identify
the natural head nodding and shaking that can
be recognized in real-time using optical flow motion tracking and find the
direction of head during the head movement for nonverbal communication.
Keywords: Haar-cascade classifier, facial expression, artificial neural network, template matching,
lucas-kanade optical flow.
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