The invention discloses a human
face detection method and a detection device based on multi-task
cascade-connection
convolution neural network, wherein the method comprises: establishing a
cascade-connection multi-level
convolution neural network; using the human face front samples, the human face back samples, some parts of the human face and the human face'
s key point samples as the training samples to
train the multi-level
convolution neural network to learn the tasks of human face categorizing, human face area position regression and human face'
s key point positioning; and utilizing the well trained multi-level convolution neural network to make human
face detection from the to-be-detected image wherein in the training stage, both the online manner and the offline manner are combined to extract the human face back samples as the training samples. According to the invention, based on the
cascade-connection multi-level convolution neural network, it is possible to learn the characteristics with stronger robustness, and at the same time, through the combination of the online manner and the offline manner to extract the back samples, the categorizing capability of the network is enhanced, so that the detection capability and the accuracy of the network are increased, and that the running speed of the method in an actual product is ensured.