Principal Component Analysis and K-Nearest Neighbor Classifier for Facial Expression Recognition System
Firas HushamAlmukhtar · International Journal of Current Research and Academic Review · 2018
In the last decades, facial expressions recognition systems become one of the most interesting research challenges. Many techniques proposed to achieve these challenges. In this paper, Principal Component Analysis (PCA) is adopted to take the facial features out of the input image, so as these features will be used for the K-Nearest Neighbor (K-NN) classifier to classify it into its nearest categories. The proposed method is tested based on two types of databases (JAFFE (Japanese Female Facial Expressions) and KDEF (The Karolinska Directed Emotional Faces)). Each of these databases has many categories of facial expressions images (6 basic face expressions and one neutral expression) labelled from No.1 to No.7 such that each number represents one class category. The experimental result shows the robustly and feasibility of our suggested system during the identification and the classification step to assign the new tested image to its suitable class label.