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BACKGROUND:Video-based face recognition has attracted much attention owning to its wide range of applications such as video surveillance. There are various approaches for facial feature extraction. Feature vectors extracted by these approaches tend to have large dimension and may include redundant information for face representation, which limits the application of methods with high accuracy such as machine learning.OBJECTIVE:Facial landmarks represent the intrinsic characteristics of human face, which can be utilized to decrease redundant information and reduce the computation complexity. But feature points extracted in each frame of a video are irregular which needed to be aligned.METHODS:This paper presents a novel method which is based on facial landmarks and machine learning. We proposed a method to align the feature data into a common co-ordinate frame, and use a robust AdaBoost algorithm for classification.RESULTS:Experiments on the public Honda/UCSD database demonstrate the superior performance of our method to several state-of-the-art approaches. Experiments on Yale database show the sensitivity and specificity of the proposed method.CONCLUSION: The proposed methods can improve the image-set based recognition performance.

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BACKGROUND: With the continuous expansion of urban scale and the increasing concentration of population, public health crisis has become an important part of urban residents’ health management. The outbreak of the COVID-19 pandemic in Wuhan in 2020 has sounded the alarm. OBJECTIVE: With the government at all levels to carry out the construction of urban Internet of things and information internet, the Internet backbone network has been built, deployed a large number of sensors, and collected a large number of urban situation data. METHODS: In this paper, situational awareness technology is introduced into public health emergency services. RESULTS: By constructing ontology, situational data and residents’ health data are integrated. Through key technologies such as situational data collection, data fusion and data mining, real-time perception of environmental conditions of public health emergency scene is realized, and situational data fusion and situational information reasoning model are constructed. CONCLUSIONS: The model is applied to the public health crisis emergency simulation system to verify the effectiveness of the model.