Automated facial and emotional recognition has been extensively applied in computer science, medical neuroscience, law enforcement and crowd monitoring. The study evaluates use of popular feature descriptors, Local Binary Pattern (LBP) and Local Directional Pattern (LDP) variants in facial expression recognition feature extraction. It then classifies results of the local facial features of major emotional states, namely neutral, anger, fear, extraction and expression identification using a combined ratio of classifiers called Voting Classifer. Databases used in the experiments involved Cohn-Kanade Database and the Googleset datasets and the expression classification rate of around 99.13% was achieved. The proposed solution included a hybrid of Local Directional Pattern (LBP), Local Directional Pattern (LDP) as the feature extraction algorithms and weighted ensemble of classifiers called voting classifier classification algorithm.
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