The paper aims at the extraction of expression features in facial expression recognition. Facial expression identification has become increasingly significant in identifying the emotions of those who are confronted with it, as well as in picture processing. In this paper dataset is used namely the FER2013 dataset, it is a popularly used dataset for exploration and practices. The FER2013 dataset is used to detect facial expressions efficiently. AFERS is applied in this case. Facial detection, face feature extraction, and facial expression recognition are the three stages of this method for detecting facial expressions. And some other methods are also used as Pre-processing and Emotion classification. This paper outlines the efficiency of various widely spread machine learning algorithms for recognizing facial expressions, namely Support Vector Machine (SVM), Naïve Bayes.