This invention relates generally to image processing and, more particularly, to object detection using machine learning. Face detection systems perform image processing on digital images or video frames to automatically identify people. In one approach, face detection systems classify images into positive images that contain faces and negative images without any faces. Face detection systems may train neural network for detecting faces and separating the faces from backgrounds. By separating faces from backgrounds, face detection systems may determine whether images contain faces. A good face detection system should have a low rate of false positive detection (i.e., erroneously detecting a negative image as a positive image) and a high rate of true positive detection (i.e. Face detection remains challenging because the number of positive images and negative images available for training typically are not balanced. For example, there may be many more negative images than positive images, and the neural network may be trained in a biased manner with too many negative images. As a result, the neural network trained with the imbalance number of positive and negative samples may suffer from low accuracy in face detection with high false positive detection rate or low true positive detection rate. Face detection also remains challenging because facial appearance may be irregular with large variance. For example, faces may be deformed because of subjects having varying poses or expressions. In addition, faces may be deformed by external settings such as lighting conditions, occlusions, etc. As a result, neural network may fail to distinguish faces from backgrounds and cause a high false positive detection rate. Thus, there is a need for good approaches to accurate face detection and detection of other objects.