Facial expression recognition using neural networks and log-Gabor filters

This study proposes a classification-based facial expression recognition method using a bank of multilayer perceptron neural networks. Six different facial expressions were considered. Firstly, logarithmic Gabor filters were applied to extract the features. Optimal subsets of features were then selected for each expression, down-sampled and further reduced in size via principal component analysis (PCA). The arrays of eigenvectors were multiplied by the original log-Gabor features to form feature arrays concatenated into six data tensors, representing training sets for different emotions. Each tensor was then used to train one of the six parallel neural networks making each network most sensitive to a different emotion. The classification efficiency of the proposed method was tested on static images from the Cohn-Kanade database. The results were compared with the full set of log-Gabor features. The average percentage of the correct classifications varied across different expressions from 31% to 85% for the optimised sub-set of log-Gabor features and from 23% to 67% for the full set of features. The average correct classification rate was increased from 52% for the full set of the log-Gabor features, to 70% for the optimised sub-set of log-Gabor features.