Facial expression has an important role in human interaction and non-verbal communication. Hence more and more applications, which automatically detect facial expressions, start to be pervasive in various fields, such as education, entertainment, psychology, human-computer interaction, behavior monitoring, just to cite a few. In this paper, we present a new approach for facial expression recognition using a so-called deep conspicuous neural network. The proposed method builds a conspicuous map of region faces, training it via a deep network. Experimental results achieved an average accuracy of 90% over the extended Cohn-Kanade data set for seven basic expressions, demonstrating the best performance against four state-of-the-art methods.