In this thesis, macro and micro facial expression sequences from various datasets are trained using neural networks to classify them in one of the basic emotions. In macro expression experiments, for each frame of the sequences facial landmarks are extracted using MediaPipe FaceMesh solution and geometric features using both spatial and temporal information based on these landmarks are created. To classify the features, ConvLSTM2D followed by multilayer perceptron blocks are used. In order to achieve real time classification performance, all algorithms are implemented compatible to run on GPU. The proposed method for macro expressions is tested with CK+, Oulu-CASIA VIS, Oulu-CASIA NIR and MMI datasets. In micro expression experiments, apart from geometric features also blendshape features provided by MediaPipe are used. In order to improve classification performance, Phase-Based Video Motion Processing technique is used to magnify subtle facial movements of micro expressions. Experiments are conducted separately on same classification layers that consist of ConvLSTM1D followed by multilayer perceptron blocks. The proposed method for micro expressions is tested with SAMM and CASME II datasets. The datasets utilized in this study were accessed upon signing corresponding license agreements. Each dataset is specifically designated for academic purposes and is made available under these agreements. Only data from subjects who provided consent for their information to be used in publications was included in the thesis. The license agreements for each dataset can be found in the appendices section.