In a video analytics for audience measurement system, dwell time, gaze, and opportunity-to-see statistics are required most of the time. To generate these statistics, more than one face detector is used in order to capture both profile and frontal faces. In this paper, we present a novel approach for face detection in video analytics. The assumption is that; the face occurrences are limited in such systems and one classifier is able to capture all of these occurrences. By using MB-LBP for feature extraction and Gentle Boost for statistical learning we trained a classifier which is able to detect both profiles and frontal faces with more than 80 % success rate. The proposed system also uses an efficient scanning algorithm and achieves 60 fps on a 720p video.