Abstract
Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.
Highlights
Advancements in the robotics field have led to the emergence of a diversity of robotbased applications and favored the integration of robots in the automation of several applications of our daily life
We trained the robot with various deep learning algorithms
The findings showed that some algorithms were well suited for the security criterion
Summary
Advancements in the robotics field have led to the emergence of a diversity of robotbased applications and favored the integration of robots in the automation of several applications of our daily life. Multi-robot systems, where several robots collaborate in the achievement of a task [1], are used in several applications including smart transportation [2], smart healthcare [3], traffic management [4], disaster management [5], and face recognition [6,7]. Robots’ resources have been improving in terms of energy, computation power, and storage, they still cannot satisfy the need of emerging applications [8]. Researchers focused on solutions that leverage the use of cloud computing [9]. A new paradigm has emerged, namely cloud robotics [8]. Cloud robotics resulted from the integration of advancement in the robotics field with the progress made in the cloud computing field
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