Facial Expressions recognition Based on Principal Component Analysis (PCA)
This work addresses facial expression recognition for applications in human character and emotion analysis, but it is incremental as it uses an existing PCA method on standard data.
The paper tackles the problem of facial expression recognition under varying conditions like illumination and occlusion by applying Principal Component Analysis (PCA) with Eigenfaces on a standard database, achieving recognition of expressions without specifying concrete performance numbers.
The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint. The process of recognizing facial expression is a hard task due to the several circumstances such as facial occlusions, face shape, illumination, face colors, and etc. This paper present a PCA methodology to distinguish expressions of faces under different circumstances and identifying it. Relies on Eigen faces technique using standard Data base images. So as to overcome the problem of difficulty to computers to identify the features and expressions of persons.