In this paper, we deal with the performance evaluation method of user identification and user tracking for intelligent robots using face images. This paper shows general approaches for standard evaluation methods to improve intelligent robot systems as well as their algorithms. The evaluation methods proposed in this paper can be combined with the evaluation methods for detection algorithms of face region and facial components to measure the overall performance of face recognition in intelligent robots.

In this paper, we present a Sound Source Localization (SSL) based GCC (Generalized Cross Correlation)–PHAT (Phase Transform) and new measurement method of angle with robot auditory system for a network-based intelligent service robot. The main goal of this paper is to analysis performance of TDOA and GCC-PHAT sound source localization method and new angle measurement method is compared. We use GCC-PHAT for measuring time delays between several microphones. And sound source location is calculated by using time delays and new measurement method of angle. The robot platform used in this work is wever-R2, which is a network-based intelligent service robot developed at Intelligent Robot Research Division in ETRI.

MOCIE(Ministry of Commerce, Industry and Energy) handles 6 Projects for Intelligent Robot, whose budget is around 40 Million dollars per year. In this paper we have tried to analyze the state of robot technology of the projects. Each sub-projects has been divided according to the technological classification. Two major projects of Next Generation Growth Engine and 21C Frontier show different state each other. The former is focused on the product while the latter on the technology. Output of 21C Frontier should be linked to the Next Generation Growth Engine, otherwise, it will fail to advance. The project management handles only the quantitative performance such as business results, number of prototype, and number of patents and papers. Technological Capability is essential and it should be managed. This paper proposes efficient classification of robot technology and technology index.

In this paper, we introduce visual contexts in terms of types and utilization methods for robust object recognition with intelligent mobile robots. One of the core technologies for intelligent robots is visual object recognition. Robust techniques are strongly required since there are many sources of visual variations such as geometric, photometric, and noise. For such requirements, we define spatial context, hierarchical context, and temporal context. According to object recognition domain, we can select such visual contextx. We also propose a unified framework which can utilize the whole contexts and validates it in real working environment. Finally, we also discuss the furture research directions of object recognition technologies for intelligent robots.