In face recognition, searching a person's face in the whole picture is generally too time-consuming to ensure high-detection accuracy. Objects similar to the human face or multi-view faces in low-resolution images may result in the failure of face recognition. To alleviate the above problems, a real-time face recognition method based on pre-identification and multi-scale classification is proposed in this study. The face area is segmented based on the proportion of human faces in the pedestrian area to reduce the search range, and faces can be robustly detected in complicated scenarios such as heads moving frequently or with large angles. To accurately recognise small-scale faces, the authors propose the multi-scale and multi-channel shallow convolution network, which combines a multi-scale mechanism on the feature map with a multi-channel convolution network for real-time face recognition. It performs face matching only in the pre-identified face areas instead of the whole image, therefore it is more efficient. Experimental results showed that the proposed real-time face recognition method detects and recognises faces correctly, and outperforms the existing methods in terms of effectiveness and efficiency.