Abstract
With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.
Highlights
Face recognition [1] is a hot topic in the computer vision research field and has been widely applied in various fields, such as identity authentication, security, and access control systems
Since the number of pedestrians appearing in the video changes over time, during the task assignment process, the amount of tasks handled by the agent changes dynamically. These changes will result in an unbalanced task allocation for the agent, which means dynamic task balancing is very important for the agent side. In view of these problems, a distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper
This paper presents a new distributed face recognition framework based on load balancing as the number of videos increases
Summary
Face recognition [1] is a hot topic in the computer vision research field and has been widely applied in various fields, such as identity authentication, security, and access control systems. The multi-scale block local binary patterns are extracted from these regions to obtain both locally and globally informative features, and the distributed framework is introduced to accelerate the recognition process It can improve the efficiency of face recognition in various lighting environments without reducing the recognition accuracy. The abovementioned distributed face recognition systems can improve the face recognition efficiency with multiple servers or clients, but at the price of increasing the computational complexity, and essentially they all achieved face recognition based on images rather than videos To this end, the agent-based distributed face recognition model was proposed in [9]. These changes will result in an unbalanced task allocation for the agent, which means dynamic task balancing is very important for the agent side In view of these problems, a distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
