Improving facial expression recognition through PCA and LBP with SVM classifier

This paper proposes a method for facial expression recognition method using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms to extract feature from facial images. Experiments were conducted on two datasets: Japanese Female Facial Expression (JAFFE) database and Cohn-Kanade Extended (CK+) database. Support Vector Machine (SVM) was used as the primary classifier, compared to Euclidean distance (L2) to evaluate the performance of the classification methods. The experimental results show that the combination of PCA and SVM achieved a recognition rate of 87% on the JAFFE database and 81% on the CK+ database, with differences due to the complexity and diversity of the CK+ dataset. The study indicates that the PCA and LBP method combined with SVM outperforms methods using Euclidean distance, providing SVM to be an effective classifier for facial expression recognition in complex experimental environments.