Method for Real Time Face Recognition Application in Unmanned Aerial Vehicles

Abstract

Newly evolving threats to public safety and security, related to attacks in public spaces, are catching the attention of both law enforcement and the general public. Such threats range from the emotional misbehaviour of sports fans in sports venues to well-planned terrorist attacks. Moreover, tools are needed to assist in the search for wanted persons. Static solutions, such as closed circuit television (CCTV), exist, but there is a need for a highly-portable, on-demand solution. Unmanned aerial vehicles (UAVs) have evolved drastically over the past decade. Developments are observed not only with regards to flight mechanisms and extended flight times but also in the imaging and image stabilization capabilities. Although different methods for facial recognition have existed for some time, dealing with imaging from a moving source to detect the faces in the crowd and compare them to an existing face database is a scientific problem that requires a complex solution. This paper deals with real-time face recognition in the crowd using unmanned aerial vehicles. Face recognition was performed using OpenCV and Dlib libraries.

Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance

“…As we noticed in Table 1 , the authors in [ 7 ] applied state-of-art deep learning techniques for face detection and recognition using conversion of low resolution images to high-quality images, but the technique is not tested in low-resolution images from large gatherings. Moreover, literature in [ 1 , 2 , 3 , 4 , 5 , 11 ] shows work on recognizing people based on large crowd and low resolution image data, whereas the literature presented in [ 12 ] only depicts exploitation of large crowd data and in [ 13 ] research carried out only on low resolution data. However, emotional expression of human face have been found in [ 4 ] crowded environment showed happy faces are easily by identified.…”

“…First, to the best of our knowledge, this is one of the few proposals for automatic tracking of the missing persons in a large gathering with low-resolution images. There are various proposals in the literature, which apply face recognition algorithms to large crowd images such as [ 1 , 2 , 3 , 4 ]; however, tracking a person in a large crowd with low-resolution images is rare. Several state-of-the-art deep learning algorithms are used for face recognition but with high-quality images such as [ 5 ]; however, our study of the related work suggested that they do not show good performance on low-resolution images of the unconstrained environment.…”

A Novel Integration of Face-Recognition Algorithms with a Soft Voting Scheme for Efficiently Tracking Missing Person in Challenging Large-Gathering Scenarios

“…Hence, we found the gap in identification of persons under the unconstrained environment having a crowd with low-resolution images and used five state-of-the-art, well-known, and well-explored algorithms to cater the above-said problem; we found a significant improvement in the efficiency of the results as compared to other work as shown in [ 8 ], which used two algorithms.…”

“…Additionally, it demonstrated that dealing with imagery from a moving source in order to recognize and compare faces in a crowd to an existing face database is a hard scientific issue that demands a sophisticated solution. The purpose of this article was to discuss real-time facial recognition with two algorithms in crowds utilizing unmanned aerial vehicles [ 8 ].…”

“… Related work exploration in identifying missing persons in a huge crowd by face detection with low-resolution images [ 8 , 9 , 10 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. …”