Recognizing expressions are a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. In the first part of the experiment, we develop computational models capable of differentiating between two human facial expressions. We perform pre-processing by Gabor filters and dimensionality reduction using the methods: Principal Component Analysis, and Curvilinear Component Analysis. Subsequently the faces are classified using a Support Vector Machines. We also asked human subjects to classify these images and then we compared the performance of the humans and the computational models. The main result is that for the Gabor pre-processed model, the probability that an individual face was classified in the given class by the computational model is inversely proportional to the reaction time for the human subjects.