TWO FACIAL EMOTION DETECTION BASED on NAIVE BAYESIAN CLASSIFIER

Emotion is an affective state of a subjective reaction in an environment accompanied by physiological and endronic changes in human beings; this happens suddenly and abruptly in the form of a crisis. In the article, Bayes' theorem's implementation was developed that allows classifying two facial emotions of the human being. Our central premise is based on realizing a Bayesian model to generate a supervised learning model, which uses the analysis of data collected to create an emotions classifier. The Naive Bayes classifier training model results provide a functional form of probability to capture joint statistics of local appearance and position on the object whose one-to-one match result is slightly higher than 56%. This value is less than the method used by Schneiderman and Kanade. Concluding that the proposed algorithm is better than those analyzed because several external variables such as lighting, pose, and detection of characteristics can change the performance in terms of precision.