## 1 Introduction  
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. With the rapid development of internet, more and more data are emerging and being shared, including increasing video data. The centralized face recognition system needs to deal with all the data, operations, and processing tasks, which is always limited by the performance of the central computer. To overcome the limitation, a higher-performance computing architecture based on GPU (Graphics Processing Unit)is proposed [2]. Instead, some researchers turned to designed distributed systems for face recognition. [PERSON] et al. [3] accomplished behavior analysis of pedestrians through lost items in a distributed architecture and obtained good performance. The distributed face recognition system can not only synchronize face recognition to multiple servers and clients, but also deal with multiple videos at the same time. It is highly efficient and can handle a large-scale face dataset very well. [PERSON] et al. [4] proposed a distributed parallel face recognition system which allocates a large-scale face database to multiple subordinate servers, and executes recognition simultaneously by these subordinate servers. This system not only can reduce overhead when adding or subtracting nodes in the array structure, but also increase infinite scalability. However, it is only suitable for large-scale face databases, and takes up a lot of hardware resources. A distributed face recognition model in wireless sensor networks was proposed in [5; 6]. The model divides a facial image into four sub-images based on facial features, and then extracts and identifies the features of sub-images separately. Similarly, another distributed framework based on multi-block local binary patterns and data fusion was proposed in [7], which divides the face area into four parts, namely two eyes, nose and mouth. Then, 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. Nevertheless, in this framework, the division of the face area is generally empirical and requires accurate manual segmentation. What is more, it also needs to deal with the problem of dividing face regions to ensure the illumination in each area is uniform. The uniform distributed face recognition system [8] could quickly recognize faces in videos based on the client/server technology. It improves the face recognition efficiency by processing each video frame in parallel, but can require heavy computer resources. 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]. The model consists of three layers, i.e., a central agent host layer for face detection, a neighboring agent host layer for feature extraction, and a remote agent host layer for face matching in parallel computing environments. In this way, the system has improved scalability and can handle a growing amount of tasks. However, when the amount of tasks among agents is quite different, the agent with more tasks will have time delay and a sharp increase in CPU utilization, thus reducing the overall efficiency. 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.