Deep-Facial Feature-Based Person Re-identification for Authentication in Surveillance Applications

Nowadays, a large network of cameras is predominantly used in public places which provide enormous video data. These data are monitored manually and may be utilized only when the need arises to ascertain the facts. Automating the system can improve the quality of surveillance and be useful for high-level surveillance tasks like person identification, suspicious activity detection or undesirable event prediction for timely alerts. In this paper, we proposed a model that can Re-identify a person from a single camera tracking environment. This system will automatically extract face features of the person and generate the Unique Id for each person when it enters for the first time in the monitored area. Its face features are stored in the database which will help to Re-identify the person whenever the same person appears again. The challenges faced by the system are occlusion, pose, light conditions, and face orientation. The proposed system highlights, effect of different deep neural networks for Person Re-identification and compares based on the accuracy, GPU usage, Speed, Number of faces detected by overcoming the challenges like illumination and occlusion. The advantage of the system is it doesn′t require the database of people in advance for recognition and it will be helpful for criminal identification for crime control and prevention