ace detection is a very important biometric application in the field of image analysis and computer vision. The basic face detection method is AdaBoost algorithm with a cascading Haar-like feature classifiers based on the framework proposed by Viola and Jones. Real-time multiple-face detection, for instance on CCTVs with high resolution, is a computation-intensive procedure. If the procedure is performed sequentially, an optimal real-time performance will not be achieved. In this paper we propose an architectural design for a parallel and multiple-face detection technique based on Viola and Jones' framework. To do this systematically, we look at the problem from 4 points of view, namely: data processing taxonomy, parallel memory architecture, the model of parallel programming, as well as the design of parallel program. We also build a prototype of the proposed parallel technique and conduct a series of experiments to investigate the gained acceleration.