Most of the human feelings are expressed through the face and by seeing one’s face, one can easily identify whether he is happy or sad or angry. So, for truly knowing the feeling behind words, the facial expression must be correctly recognized. Facial expression extracts the true or inner emotion which the speaker tries to hide and find out whether he is happy/sad/angry/neutral/surprised. Its applications include human behavior detection, human-computer interaction, security, lie detection, pain detection, music system based on mood, automated tutoring, expressing through emoticons, etc.
Build a Raspberry-Pi based standalone edge device that can detect real-time facial emotions using a dataset of employees working in organizations. A Raspberry Pi-based standalone edge device has been implemented using the OpenCV object detectors with HAAR cascade classifier to extract faces from images and pretrained model VGG-19 classifier to train a multi-class predictor for classifying the five fundamental human facial expressions. This device has achieved 96% accuracy for detecting faces in real-time
