Face recognition in low resolution images has challenges in active research because face recognition is usually implemented in high resolution images (HR). In general, research leads to a combination of pre-processing and training models. Therefore, this study aims to classify low-resolution face images using a combination of pre-processing and deep learning. In addition, this study also aims to compare evaluation results based on differences in epoch values. In this research will use the Trunk Branch Ensemble-Convolutional Neural Network (TBE-CNN) as one of Convolutional Neural Network (CNN) architecture combined with the pre-processing method which is super resolution to get better accuracy compared to the state-of-art. Then the model is trained with different epoch values, that is 40 and 100, to comparing the best classification. This study was evaluated by YTD Dataset. Based on the test results, this proposed method has achieved better results compared to the previous method, which gives an increase of 1%. After that, training results from different epoch values used produce different accuracy at the training model but have no effect on validation and testing model. The training using epoch = 100 has an accuracy increase of 3% compared to training using epoch = 40 and the loss ratio obtained at the training is decreased by 20% compared to training at epoch = 40. Furthermore, optimization parameters used and reducing computing time when training models and validation will be future research.