Companies have long been interested in gauging the customer’s level of interest in their advertisements. By analyzing the gaze direction of individuals viewing a public advertisement, we can infer their level of engagement. Head pose detection allows us to deduce pertinent information about gaze direction. Using video sensors, machine learning methods, and image processing techniques, information pertaining to the head pose of people viewing advertisements can be automatically collected and mined. We propose a method for the coarse classification of head pose from low-resolution images in crowded scenes captured through a single camera and under different lighting conditions. Our method improves on the technique described in [1]; we introduce several modifications to the latter scheme to improve classification accuracy. First, we devise a mechanism that uses a cascade of three binary Support Vector Machines (SVM) classifiers instead of a single multi-class classifier. Second, we employ a bigger dataset for training by combining eight publicly available databases. Third, we use two sets of appearance features, Similarity Distance Map (SDM) and Gabor Wavelet (GW), to train the SVM classifiers. The scheme is tested with cross validation using the dataset and on videos we collected in a lab experiment. We found a significant improvement in the results achieved by the proposed method over existing schemes, especially for video pose classification. The results show that the proposed method is more robust under varying light conditions and facial expressions and in the presences of facial accessories compared to [1].