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The development and comparison of face recognition algorithms based on different technical characteristics

机译：基于不同技术特征的人脸识别算法的开发与比较

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摘要

Face recognition technology is the classic issue in the computer vision field. As the most significant biological feature of human beings, human face has attracted the wide attention of large amount of researchers. Although, the continuously emerging new face recognition algorithms have their unique benefits, the reference significance of classic old algorithms cannot be ignored. Therefore, the regular conclusion of face recognition algorithms is very necessary. In the essay, the classification is conducted based on the development history and major technology characteristics of face recognition technology. The essay separately introduces and comparatively analyzes the three representative traditional face recognition algorithms which are template matching technology, AdaBoost framework and DPM model. In addition, it also introduces the deep-learning face recognition algorithms, including R-CNN, Cascade CNN, DenseBox, MTCNN and YOLO. And then, the advantages and disadvantages of each algorithm are separately described.
机译：面部识别技术是计算机视觉领域的经典问题。作为人类最重要的生物特征，人类的脸引起了大量研究人员的广泛关注。虽然，连续新兴的新面貌识别算法具有它们独特的好处，但经典旧算法的参考意义不能忽视。因此，面部识别算法的正则结论是非常必要的。在论文中，分类是基于人脸识别技术的开发历史和主要技术特征进行。本文分别介绍并相对分析了作为模板匹配技术，Adaboost框架和DPM模型的三个代表性的传统人脸识别算法。此外，它还介绍了深度学习的人脸识别算法，包括R-CNN，级联CNN，DenseBox，MTCNN和YOLO。然后，单独描述每种算法的优点和缺点。

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