This will work alot better, but will be more challenging to implement. Try to use the eye and mouth models of openCV to detect them inside the face region. Second approach asks more reading of papers, google on face landmarks. Then match vectors using a distance measure to find the best match. Related keywords: balloon balloons happy face happy happiness smiley smileys smiley face sad sadness sad face frowny face bad energy bad vibes atmosphere bad atmosphere. ) creating a unique representation of each mood and fitting a codebook featurevector to it. Categories: Entertainment Health/Beauty Women. Detect interesting face points, like nose tip, mouth corners, eye locations, closed/open lids and determine a relation between this elements for each mood.įirst approach is possible by applying an abstract representation of the image (eigenFaces, fisherFaces.Match the head image versus a database of images of emotions, try to find the closest matching element and assign the same classification/label.Once you have detected the region you can go to psosible directions For the first item, you can simply use the Viola&Jones platform, which is available in OpenCV, and which can be easily adapted to match your needs.
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