Department of Computer Technology and Computation, University of Alicante, Alicante, Spain.
Institute of International Studies (ISM), SGH Warsaw School of Economics, Warsaw, Poland.
Sci Rep. 2023 Sep 7;13(1):14713. doi: 10.1038/s41598-023-40110-y.
Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
智能监控和监测系统已成为智能城市安全应用领域的一个重要方面。特别是，应用于潜在罪犯识别以及检测和预防暴力行为的人体活动识别（HAR）视频监控仍然是一个具有挑战性的任务。本文提出了一种基于深度学习的用于安全应用的远距离人脸识别方法。由于缺乏远距离人脸识别的可用数据集，引入了一种生成可靠数据集的方法，该数据集将个体与摄像机的距离、图像传感器的焦距和目标面部的像素大小相关联。为了生成扩展数据集，选择了佐治亚理工学院远距离人脸数据库和人脸质量数据库。然后，使用该数据集对一组用于监控摄像机的商业图像传感器进行测试和应用。该系统在多个传感器上的平均准确率超过 99%，并且可以计算出传感器的最大距离，以在识别中获得所需的准确性，这在智能城市的安全应用中可能至关重要。
