Facial Expression Recognition using Deep Learning

Facial expression recognition (FER) is now getting extensively popular because of its ability to predict an unknown data-set, and to its extent with some accuracy. An average human being possesses or shows seven different expressions based on the situation, namely anger, sad, happy, surprise, disgust, neutral and scared. Each individual has a unique way to express the afore-mentioned expressions and hence the term “an unknown data-set”. To identify human’s present mindset through facial expressions, many data sets are prepared based on face components (such as lips, cheek, nose, eyes and eye brows etc.,) dislocations and elasticity of all the facial parts. Many facial recognition systems are functioning on muscle distribution analysis from the mother image set’s pixel parameters. This research paper is going to present about image pre processing, facial expression learning methods, classification methods and implementation of Face Ex algorithm for facial expression analysis through FER2013 CNN data sets and Viola-Jones Principle.