Deep learningbased intelligent face recognition in IoTcloud environment Abstract   In recent years, the InternetofThings (IoT) technology is being used in many application areas such as healthcare, video surveillance, transportation etc. The massive adoption and growth of IoT in these areas are generating a massive amount of data. For example, IoT devices such as cameras are generating a huge amount of images when used in hospital surveillance scenarios. Here, face recognition is an important element that can be used for securing hospital facilities, emotion detection and sentiment analysis of patients, detecting patient fraud, and hospital traffic pattern analysis. Automatic and intelligent face recognition systems have high accuracy in a controlled environment; however, they have low accuracy in an uncontrolled environment. Also, the systems need to operate in realtime in many applications such as smart healthcare. This paper suggests a treebased deep model for automatic face recognition in a cloud environment. The proposed deep model is computationally less expensive without compromising the accuracy. In the model, an input volume is split into several volumes, where a tree is constructed for each volume. A tree is defined by its branching factor and height. Each branch is represented by a residual function, which is constituted by a convolutional layer, a batch normalization, and a nonlinear function. The proposed model is evaluated in various publicly available databases. A comparison of performance is also done with stateoftheart deep models for face recognition. The results of the experiments demonstrate that the proposed model achieved accuracies of 98.65%, 99.19%, 95.84% on FEI, ORL, and LFW databases, respectively.

Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in realtime applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in realtime applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.