An AdaBoost-Based Face Detection System Using Parallel Configurable Architecture With Optimized Computation

Abstract

With the development of image sensor technology, the AdaBoost-based face detections are widely used in many monitoring sensor networks and mobile-camera-based applications. Fast face detection with high detection accuracy and low power consumption in such kinds of applications is very important. Since the AdaBoost-based face detection exhibits characteristics of data computation in dual direction and data diversity, we propose an AdaBoost-based face detection system using parallel configurable architecture with optimized computation. The architecture consists of parallel configurable arrays and two-level shared memory systems. It achieves dual-direction-based integral image computation that improves parallel processing efficiency and enables the subwindow adaptive cascade classification for data diversity, which further improves the detection efficiency in diverse face detection. Compared with the state-of-the-art works, this work achieves maximal performance of 30 frames/s at 1080p detection speed and extreme low power consumption.