Baseline CNN structure analysis for facial expression recognition

We present a baseline convolutional neural network (CNN) structure and image preprocessing methodology to improve facial expression recognition algorithm using CNN. To analyze the most efficient network structure, we investigated four network structures that are known to show good performance in facial expression recognition. Moreover, we also investigated the effect of input image preprocessing methods. Five types of data input (raw, histogram equalization, isotropic smoothing, diffusion-based normalization, difference of Gaussian) were tested, and the accuracy was compared. We trained 20 different CNN models (4 networks x 5 data input types) and verified the performance of each network with test images from five different databases. The experiment result showed that a three-layer structure consisting of a simple convolutional and a max pooling layer with histogram equalization image input was the most efficient. We describe the detailed training procedure and analyze the result of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc

Integrated navigation system for indoor service robots in large-scale environments

Abstract- This paper describes an integrated navigation strategy for the autonomous service robot in large-scale indoor environments. It includes architecture of navigation system, the development of crucial navigation algorithms like map, path planning, and localization, and planning scheme such as error/fault handling. Major advantages of proposed navigation are as follows: 1) A range sensor based generalized scheme of navigation without modification of the environment. 2) Intelligent navigation-related components. 3) Framework supporting the selection of multiple behaviors and error/fault handling schemes. A experimental result shows the feasibility of proposed navigation system. The result of this research has been successfully applied to our three service robots in a variety of task domains including a delivery, a patrol, a guide, and a floor cleaning task

Tripodal Schematic Design of the Control Architecture for the Service Robot PSR

Abstract − This paper describes a control architecture design and a system integration strategy for the autonomous service robot PSR(Public Service Robot). The PSR is under development at the KIST(Korea Institute of Science and Technology) for service tasks in public spaces such as office buildings and hospitals. The proposed control architecture is designed by tripodal frameworks, which are layered functionality diagram, class diagram, and configuration diagram. The tripodal schematic design clearly points out the way of integrating various hardware and software components. The developed strategy is implemented on the PSR and successfully tested. 1