Face recognition plays an import role in our daily lives. However, computer face recognition performance degrades dramatically with the presence of variations in illumination, head pose and occlusion. In contrast, the human brain can recognize target faces over a much wider range of conditions. In this paper, we investigate target face detection through electroencephalography (EEG). We address the problem of single-trial target-face detection in a rapid serial visual presentation (RSVP) paradigm. Whereas most previous approaches used support vector machines (SVMs), we use a convolutional neural network (CNN) to classify EEG signals when subjects view target and non-target face stimuli. The CNN outperforms the SVM algorithm, which is commonly used for event-related-potential (ERP) detection. We also compare the difference in performance when using animal stimuli. The proposed system can be potentially used in rapid face recognition system.