Benjamin Johnston

Siva Leela Krishna Chand Gudi, Benjamin Johnston, Mary-Anne Williams

Human-robot interaction plays a crucial role to make robots closer to humans. Usually, robots are limited by their own capabilities. Therefore, they utilise Cloud Robotics to enhance their dexterity. Its ability includes the sharing of information such as maps, images and the processing power. This whole process involves distributing data which intend to rise enormously. New issues can arise such as bandwidth, network congestion at backhaul and fronthaul systems resulting in high latency. Thus, it can make an impact on seamless connectivity between the robots, users and the cloud. Also, a robot may not accomplish its goal successfully within a stipulated time. As a consequence, Cloud Robotics cannot be in a position to handle the traffic imposed by robots. On the contrary, impending Fog Robotics can act as a solution by solving major problems of Cloud Robotics. Therefore to check its feasibility, we discuss the need and architectures of Fog Robotics in this paper. To evaluate the architectures, we used a realistic scenario of Fog Robotics by comparing them with Cloud Robotics. Next, latency is chosen as the primary factor for validating the effectiveness of the system. Besides, we utilised real-time latency using Pepper robot, Fog robot server and the Cloud server. Experimental results show that Fog Robotics reduces latency significantly compared to Cloud Robotics. Moreover, advantages, challenges and future scope of the Fog Robotics system is further discussed.

Benjamin Johnston, Philip de Chazal

We present a fully automated system for sizing nasal Positive Airway Pressure (PAP) masks. The system is comprised of a mix of HOG object detectors as well as multiple convolutional neural network stages for facial landmark detection. The models were trained using samples from the publicly available PUT and MUCT datasets while transfer learning was also employed to improve the performance of the models on facial photographs of actual PAP mask users. The fully automated system demonstrated an overall accuracy of 64.71% in correctly selecting the appropriate mask size and 86.1% accuracy sizing within 1 mask size.

Jonathan Vitale, Mary-Anne Williams, Benjamin Johnston

Valentine's face-space suggests that faces are represented in a psychological multidimensional space according to their perceived properties. However, the proposed framework was initially designed as an account of invariant facial features only, and explanations for dynamic features representation were neglected. In this paper we propose, develop and evaluate a computational model for a twofold structure of the face-space, able to unify both identity and expression representations in a single implemented model. To capture both invariant and dynamic facial features we introduce the face-space duality hypothesis and subsequently validate it through a mathematical presentation using a general approach to dimensionality reduction. Two experiments with real facial images show that the proposed face-space: (1) supports both identity and expression recognition, and (2) has a twofold structure anticipated by our formal argument.