Abstract:

With the gradual expansion of the application field of face recognition, face detection in occlusion environment is facing certain technical challenges. Because of its strong learning ability, deep learning method has become a better solution to the problem of face occlusion detection, but it still faces many problems to be solved. Reducing the influence of occlusion on the performance of detection algorithm is one of the key and difficult problems in this field. This paper analyzes the related research progress from the perspective of model, algorithm and data set, compares the basic principles of different algorithms, model performance, advantages and disadvantages and existing problems, and discusses the possible research direction in the future.

Key words: face recognition, occlusion, feature, convolution neural network, countermeasure generation network, Artificial Intelligence（AI）, deep learning

摘要：

随着人脸识别应用领域的逐渐扩大，有遮挡环境下的人脸识别面临着一定的技术挑战。深度学习方法由于其具有强大的学习能力，成为解决有遮挡环境下的人脸识别问题的一种较好的解决方案，但仍面临诸多待解决的问题。减少遮挡对人脸识别算法带来的性能影响是该领域的重点和难点问题之一。从模型、算法和数据集的角度分析了近年来相关研究进展；对比了不同算法的性能结构、优缺点以及存在的问题；探讨了未来可能的研究方向。

关键词: 人脸识别, 遮挡, 特征, 卷积神经网络, 对抗生成网络, 人工智能, 深度学习