Cloud-based face recognition for low resource clients

Face recognition-based applications are very state-of-the art methods for verification and authentication. However, many solutions are desktop-based and require very high computational machines. In this chapter, we propose a cloud-based framework for face recognition that provides central control for feature database. The framework easily swaps computational processes to the client if needed or requested. The client's machines request and respond to the feature databases using APIs that are designed using FLASK. The use of feature database makes program initialization very fast and also reliable in the case of any disaster to the client sites. In this chapter, the authors propose a cloud-based framework for face recognition that provides central control for feature database. There are a number of applications in security, education, and daily life operations where face recognition-based systems can play a vital role. Cloud-based facial acknowledgment frameworks achieve different advantages coming from natural attributes. In the IOT environment, the detection and resolution of the object is challenging to authenticate the identity of the object, manage the access of service, and establish loyalty between service of cloud and object. The classical implementation requires very expensive and sophisticated hardware, whereas in the people case, adding light clients can save a lot of unnecessary computation. Experiments show that there is significant computational gain by using the proposed framework.