1. A system to recognize objects in an image, the system comprising: an object detection network to detect an object in an input image, the object detection network comprising a first hierarchical convolutional neural network to output a first hierarchical-calculated feature for the detected object; a face alignment regression network to determine a regression loss for alignment parameters based on the first hierarchical-calculated feature for the detected object, the alignment parameters being one or more of a keypoint, a tilt angle and an affine transformation parameter for the first hierarchical-calculated feature; and a detection box regression network to determine a regression loss for detected boxes based on the first hierarchical-calculated feature for the detected object, each detected box being a region of interest in the input image, wherein the object detection network further comprises: a weighted loss generator to generate a multi-task loss function comprising a first weighted loss for the first hierarchical-calculated feature for the detected object, a second weighted loss for the regression loss for the alignment parameters for the first hierarchical-calculated feature for the detected object, and a third weighted loss for the regression loss of the detected boxes for the first hierarchical-calculated feature for the detected object; a backpropagator to backpropagate the first weighted loss, the second weighted loss and the third weighed loss; and a grouping network to form, based on the first hierarchical-calculated feature for the detected object, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.