Real‐time face recognition based on pre‐identification and multi‐scale classification

Abstract

In face recognition, searching a person's face in the whole picture is generally too time-consuming to ensure highdetection 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 multiscale 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.

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

“…Then the server allocates the task volume of the agent based on the predicted information. The experimental results show that compared with the particle swarm optimization Also, the experiments are simulated by assigning 8,12,16,18,20, and 28 videos to four agents, and the results are shown in Figure 9b. In random allocation, the processing time increases rapidly as the number of videos increases.…”

“…Face recognition [16][17][18] is an important research topic in computer vision and has received substantial attention from both research communities and the industry. Nowadays, face recognition has been able to recognize faces with high accuracy in real-time monitoring [19][20][21][22], e.g., face recognition time attendance machines in some companies or supermarkets, intelligent alarm systems of public security organs to track the suspects, intelligent prisoner alarm systems for prison management, security check in train stations or the airport, and so on.…”

“…Firstly, the agent records the number of pedestrians in the video and uses the number in the first n frames as a prediction sample. After getting the output weight by ELM, the number of pedestrians in the video is calculated according to Equation (16). Then, the server performs the task balancing operation according to the prediction results sent by the agent.…”

“…In order to avoid human interference and make the agent's task amount more objectively, the experiments were simulated by randomly assigning 6,8,12,16,20,24, and 28 videos to four agents. The total number of pedestrians in all the videos handled by four agents was 10, 16, 24, 31, 37, 42, and 49.…”

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

“…Multitask Cascaded Convolutional Networks (MTCNN) [45] is a multi-task cascade network to detect faces by three stages in a coarse-to-fine detection structure. Min et al [46] proposed a multi-scale and multi-channel shallow convolutional network (MMSC) for real-time face detection after the pre-identified method detecting faces in the images based on a traditional pedestrian detection method. Hu et al [47] proposed a multi-task detector with hybrid resolutions (HR), which detects different face scales from multiple layers of a single neural network.…”