Emotions play an important role in our life as a response to our interactions with others, decisions, and so on. Among various emotional signals, facial expression is one of the most powerful and natural means for humans to convey their emotions and intentions, and it has the advantage of easily obtaining information using only a camera, so facial expression-based emotional research is being actively conducted. Facial expression recognition(FER) have been studied by classifying them into seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and normal. Before the appearance of deep learning, handcrafted feature extractors and simple classifiers such as SVM, Adaboost was used to extracted Facial emotion. With the advent of deep learning, it is now possible to extract facial expression without using feature extractors. Despite its excellent performance in FER research, it is still challenging task due to external factors such as occlusion, illumination, and pose, and similarity problems between different facial expressions. In this paper, we propose a method of training through a ResNet [1] and Visual Transformer [2] called FViT and using Histogram of Oriented Gradients(HOGs) [3] data to solve the similarity problem between facial expressions.