Real-Time and Low-Memory Multi-Faces Detection System Design With Naive Bayes Classifier Implemented on FPGA

In recent years, face detection has been widely applied to a variety of fields, such as face recognition, image focusing, and surveillance systems. This study proposes a real-time multi-faces detection system based on naive Bayesian classifier using Field Programmable Gate Array (FPGA). The system includes three main parts, feature extraction, candidate face detection, and false elimination. First, downscale the image to the image pyramid and extract local binary image features from each downscaling image. With the bit-plane slicing for Local Binary Pattern (LBP) can save the memory consumption and speed up the computation. Then, adopt the naive Bayesian classifier to identify candidate faces. Finally use skin color filter and face overlapping elimination to remove false positives. The experimental results show that the accuracy rate is up to 96.14% in face detection, which demonstrates the proposed real-time multi-faces detection system is indeed effective and efficient.