Abstract: A news video can be modeled using the stratification approach by identifying, among other entities, human faces appearing in the video stream. To facilitate this, we need to develop techniques to detect and track human faces in video. This paper presents a frontal face detection method that uses the gradient energy representation extracted directly from the MPEG video. The gradient energy representation permits pertinent facial features of high contrast, such as the eyes, nose and mouth, to be highlighted. A rule-based classifier and a neural-network-based classifier are designed to classify a gradient energy pattern as face or non-face. The parameters for the two classifiers are learnt from face and non-face samples. First, we use the gradient energy face model to locate potential face regions at multiple scales and locations. Second, we perform skin-color verification to eliminate falsely detected regions. The main contribution of this work is in developing an efficient scale and position invariant method to detect faces that operates in a transformed gradient energy space in a compressed domain. The system was tested on selected video clips from an MPEG-7 data set and was found to be effective.