We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify
all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the
mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram
equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that
circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and
were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our
proposed algorithm was tested using a published database consisting of emotions from several persons. The final results
were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the
seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different
expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting
emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only
one person, we have successfully extended our GA based solution to include several subjects.
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