Improving facial expression recognition through PCA and LBP with SVM classifier

Tóm tắt

This paper proposes a facial expression recognition method using a combination of Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms for feature extraction from facial images. Experiments were conducted on two datasets: the Japanese Female Facial Expression (JAFFE) database and the 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 achieves a recognition rate of 87% on the JAFFE database and 81% on the CK+ database, with the difference attributed 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, demonstrating that SVM is an effective classifier for facial expression recognition in complex experimental setting.