Local binary pattern based facial expression recognition using Self-organizing Map

This paper presents an appearance feature based facial expression recognition system using Kohonen Self-Organizing Map (KSOM). Appearance features are extracted using uniform Local binary patterns (LBPs) from equally sub-divided blocks applied over face image. The dimensionality of the LBP feature vector is further reduced using principal component analysis (PCA) to remove the redundant data that leads to unnecessary computation cost. Using our proposed KSOM based classification approach, we train only 59 dimensional LBP features extracted from whole facial region. The classifier is designed to categorize six basic facial expressions (happiness, sadness, disgust, anger, surprise and fear). To validate the performance of the reduced 59 dimensional LBP feature vector, we also train the original data of dimension 944 using the KSOM. The results demonstrates, that with marginal degradation in overall recognition performance, the reduced 59 dimensional data obtains very good classification results. The paper also presents three more comparative studies based on widely used classifiers like; Support vector machine (SVM), Radial basis functions network (RBFN) and Multi-layer perceptron (MLP3). Our KSOM based approach outperforms all other classification methods with average recognition accuracy 69.18%. Whereas, the average recognition rated obtained by SVM, RBFN and MLP3 are 65.78%, 68.09% and 62.73% respectively.