FACIAL RECOGNIZATION

Facial expressions convey non-verbal cues,which play an important role in inter-personal
relations. Automatic recognition on facial expressions can be an important component of natural
human machine interfaces: it may also be used in behavioral science and clinical practices.It can
also be improvised to be used in applications like a dynamic music to play songs in ones’playlist
based on ones’ emotion. The proposal is to detect an emotion based on facial expressions. It is
done by taking an image as an input and classifying player the emotion of the face in the image.It
Categorizes the image into one of the basic prototype expressions
namely,Happiness,Sadness,Anger, Disust and Neutral (Class Labels).
The working involves three major steps: Face Detection, Feature Extraction and
Classification. The dataset used for the whole process is Cohn-Kandace (CK) Dataset. OpenCV
Libray in python is used for face Detection which has four pre-trained classifiers. Thevital facial
parts likecorners of mouth, cheeks,eyes, and area around are. Considered for Feature Extraction.