Abstract
At present, the automatic attendance mode of distance education is not conducive to the confirmation and analysis of information after class. In order to study the effective automatic recognition algorithm of remote education classroom, this study takes the educational classroom of intelligent innovation and entrepreneurship of Internet + as an example for analysis. Moreover, this paper adopts facial features as the basis of recognition, establishes corresponding positioning points, and constructs precise positioning methods for real-time feature capture. At the same time, the ASM algorithm is used to extract facial features, and the algorithm is improved to improve the extraction effect. In addition, this paper proposes Gabor-wavelet packet set and Gabor beamlet set for auxiliary recognition, which improves the recognition rate. Finally, this paper designs experiments to analyze the performance of the algorithm of this study. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for subsequent related research.
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