This paper proposes the design of a Facial Expression Recognition (FER) system using facial parts that is based on a deep convolutional neural network. This paper discusses a simple solution for facial expression recognition that employs a combination of algorithms for face detection, feature extraction, and classification. The proposed method employs a twochannel convolutional neural network with Facial Parts (FPs) as input to the first convolutional layer, extracted eyes as input to the first channel, and the mouth as input to the second channel. Information from both channels converges in a fully connected layer, which is then used to learn global information from these local.