People, as social beings, depend on communicating with other people. Most of the communication is made up of non-verbal components, and emotions, as well as facial expressions in general, carry a large part of that information. Emotion recognition is an important tool for increasing the quality of human-robot interaction, but it also finds usage in other areas. Many highly stressful jobs require additional precautions in the workplace, as well as during selection, encouraging research into the usefulness of information on emotions in recognizing more complex states such as fatigue, stress or cognitive overload. For the purpose of developing an emotion recognition tool using robotic vision and deep learning, literature on emotion, databases and deep learning has been studied in detail. Different approaches to the classification of emotions and different models of describing emotions in the context of facial expressions have been described. Public databases which are most commonly used in the research field of facial expression recognition have been mentioned, with a highlight on the CK+ database which has been used for the training and evaluation of the network. Most attention has been devoted to deep learning, with an emphasis on convolutional neural networks that have been used as the basis for creating this tool. All models which have been trained were listed along with the results and all relevant measures of model evaluation. The results of the models have been compared with the results of related research on the above-mentioned databases. Despite a scarce set of data, results which measure up to the best results in related research have been achieved.