Naive Bayes Classification Based Facial Expression Recognition With Kernel PCA Features

  Abita Devi,  Kantesh Kumar Gaurav,  Heena Gupta

This work presents a system which automatically identifies the emotion or expression represented on face. Various channels such as action, speech, poses, facial expression are considered as that conveys human emotion. In order to discover the connection among these channels and emotions, wide spread research has been carried out. Therefore, to classify the universal emotions, a neural network based solution is used which is combined with the image processing. Universal emotion is considered as anger, sadness, happiness, fear and surprise. Face images which are colored are provided as input to the system. When the face is detected then the feature point extraction approach is utilized to remove a group of selected feature points. Finally, a set of values is acquired and after pre-processing stage, feature which are extracted those are provided as input to the neural network to identify.

Keywords- ANN, Naïve Bayes, features, KPCA.

Unique Identification Number - IJEDR1703046
Page Number(s) - 326-330
Pubished in - Volume 5 | Issue 3 | July 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Abita Devi,  Kantesh Kumar Gaurav,  Heena Gupta,   "Naive Bayes Classification Based Facial Expression Recognition With Kernel PCA Features", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 3, pp.326-330, July 2017, Available at :http://www.ijedr.org/papers/IJEDR1703046.pdf