Face video retrieval is an attractive research topic in computer vision. However, it remains challenges to overcome because of the significant variation in pose changes, illumination conditions, occlusions, and facial expressions. In video content analysis, face recognition has been playing a vital role. Besides, deep neural networks are being actively studied, and deep learning models have been widely used for object detection, especially for face recognition. Therefore, this study proposes a cloud-based face video retrieval system with deep learning. First, a dataset is collected and pre-processed. To produce a useful dataset for the CNN models, blurry images are removed, and face alignment is implemented on the remaining images. Then the final dataset is constructed and used to pre-train the CNN models (VGGFace, ArcFace, and FaceNet) for face recognition. We compare the results of these three models and choose the most efficient one to develop the system. To implement a query, users can type in the name of a person. If the system detects a new person, it performs enrolling that person. Finally, the result is a list of images and time associated with those images. In addition, a system prototype is implemented to verify the feasibility of the proposed system. Experimental results demonstrate that this system outperforms in terms of recognition accuracy and computational time.

中文翻译：

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基于云的深度学习人脸视频检索系统

人脸视频检索是计算机视觉中一个有吸引力的研究课题。然而，由于姿势变化、光照条件、遮挡和面部表情的显着变化，它仍然是需要克服的挑战。在视频内容分析中，人脸识别一直发挥着至关重要的作用。此外，正在积极研究深度神经网络，深度学习模型已广泛用于对象检测，尤其是人脸识别。因此，本研究提出了一种基于云的深度学习人脸视频检索系统。首先，收集和预处理数据集。为了为 CNN 模型生成有用的数据集，去除了模糊图像，并在剩余图像上实现了人脸对齐。然后构建最终数据集并用于预训练 CNN 模型（VGGFace、ArcFace、和 FaceNet）进行人脸识别。我们比较这三种模型的结果，并选择最有效的一种来开发系统。为了实现查询，用户可以输入一个人的名字。如果系统检测到一个新人，它会执行登记该人。最后，结果是与这些图像相关联的图像和时间的列表。此外，还实现了系统原型，以验证所提出系统的可行性。实验结果表明，该系统在识别精度和计算时间方面均优于其他系统。结果是与这些图像相关联的图像和时间列表。此外，还实现了系统原型，以验证所提出系统的可行性。实验结果表明，该系统在识别精度和计算时间方面均优于其他系统。结果是与这些图像相关联的图像和时间列表。此外，还实现了系统原型，以验证所提出系统的可行性。实验结果表明，该系统在识别精度和计算时间方面均优于其他系统。