FPGA based real time face detection using Adaboost and histogram equalization

This paper introduces a novel technique to detect faces in real-time with very high detection rate. It is essentially a feature-based approach, in which a classifier is trained for Haar-like rectangular features selected by AdaBoost algorithm and efficient representation method histogram equalization is used for varying illumination in the image. The face detection system generates an integral image window to perform a Haar feature classification during one clock cycle. And then it performs classification operations in parallel using Haar classifiers to detect a face in the image sequence. The classifiers in the beginning of the cascade are simpler and consist of smaller numbers of features. However, as one proceeds in the cascade, the classifiers become more complex. A region is reported as detection only if it passes all the classifier stages in the cascade. If it is rejected at any stage, it is discarded and not processed further. If all stages are passed the face of a candidate is concluded to be recognized face.