Application and Development Trend of Computer Science in Face Completion

Qingguo Chen

doi:10.54097/fcis.v2i1.2483

Authors

Qingguo Chen

DOI:

https://doi.org/10.54097/fcis.v2i1.2483

Keywords:

Face Completion, Deep Learning, Generative Adversarial Networks

Abstract

In recent years, with the development of computer vision technology and the arrival of the covid-19 epidemic in daily life, the demand for occluded face recognition is increasing rapidly, and the demand for face completion technology in actual production and living scenes is growing rapidly too. However, there are still some problems in face completion technology, such as complex procedures, large amount of computation, long time required, and fuzzy details. Therefore, this paper reviews the development process of face completion technology, briefly describes the basic principle and defects of face completion technology, and summarizes the problems existing in the existing computer-based face repair technology, such as large amount of computation, long training time, blurred image details, and inaccurate pain points in the occlusion layer, and prospects the future development. These problems can be improved by optimizing algorithm, adopting new algorithm, adding new network and adding image preprocessing stage.

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