This paper presents a user recognition system, using face, height, and clothes color features under the special assumption that is a user is monitored and tracked. In real human-robot interaction situation, all information cannot be provided at the same time and some parts of frames in a video have no clues at all. In the proposed system, tracking is an important feature to recognize a user because data in the previous frames can be utilized. We propose an information update method that efficiently updates similarity results. This system is tested using the movie clips acquired under the unconstrained environment including illumination variation, several distance from a camera to the user, and various view types of human body.

In this study, we present a simple on-line speaker enrollment and identification among human-robot interaction (HRI) with intelligent service robots. For this purpose, speaker enrollment is performed through rhythmical singing voices or a simple game such as paper-scissors-rock. While the conventional enrollment methods frequently used in the security area should be cooperative, the proposed approach can be enrolled in a very natural way. After enrolling, the text-independent speaker recognition is accomplished by using the well-known mel-frequency cepstral coefficients (MFCC) and Gaussian mixture models (GMM). The experimental results reveal that the proposed approach yields better recognition performance in comparison to the results obtained by the conventional enrollment method.

This paper describes a method to recognize the age and gender of a user on the basis of human speech. Using voice source characteristics of the Mel frequency cepstral coefficients (MFCCs), a Gaussian mixture model (GMM) technique is applied in an effort to discover the age, gender, and other information as regards a user. On the basis of this information, service applications for robots can satisfy users by offering services adaptive to the special needs of specific user groups that may include adults and children as well as females and males. The major aim of this paper is to discover the voice source parameters of age and gender and to classify these two characteristics simultaneously. ETRI-VoiceDB2006 was employed to evaluate the proposed method.

User recognition is one of the most fundamental functionalities for intelligent service robots. However, in robot applications, the conditions are far severer compared to the traditional biometric security systems. The robots should be able to recognize users non-intrusively, which confines the available biometric features to face and voice. Also, the robots are expected to recognize users from relatively afar, which inevitably deteriorates the accuracy of each recognition module. In this paper, we tried to improve the overall accuracy by integrating the evidences issued by independently developed face and speaker recognition modules. Each recognition module exhibits different statistical characteristics in representing its confidence of the recognition. Therefore, it is essential to transform the evidences to a normalized form to integrate the results. This paper introduces a novel approach to integrate mutually independent multiple evidences to achieve an improved performance. Typical approach to this problem is to model the statistical characteristics of the evidences by well-known parametric form such as Gaussian. Using Mahalanobis distance is a good example. However, the characteristics of the evidences often do not fit into the parametric models, which results in performance degradation. To overcome this problem, we adopted a discrete PDF that can model the statistical characteristics as it is. To confirm the validity of the proposed method, we used a multi-modal database that consists of 10 registered users and 550 probe data. Each probe data contains face image and voice signal. Face and speaker recognition modules are applied to generate respective evidences. The experiment showed an improvement of 11.27% in accuracy compared to the individual recognizers, which is 2.72% better than the traditional Mahalanobis distance approach.

This video is concerned with a scenario for developing speech-based Human-Robot Interaction (sHRI) components for Ubiquitous Robot Companion (URC) Intelligent Service Robots as one of the next-generation growth engine industries in Korea. Here the URC means that it will provide the necessary services at any time and place to meet the user's requirements. Thus, it combines the network function with the current concept of a robot in order to enhance mobility and human interface. The main characteristics of this video are to combine of text-independent speaker recognition and Korean-based spontaneous speech recognition from two speakers in scenario. Especially each speaker communicates with service robot through spontaneous speech recognition with continuous words to provide useful information such as daily life schedule and TV program suitable to the speaker recognized. On the basis of these components, consumers will be able to utilize various speech-based services of the robot.

This paper proposes a novel real-time robust hand tracking algorithm, integrating multi-cues, and a limb's degree of freedom. For this purpose, we construct a limb model and maintain the model obtained from KLT-AR methods with respect to second-order auto-regression model and Kanade-Lucas-Tomasi (KLT) features, respectively. Furthermore, this method provides directivity of a target, enabling us to predict the next motion. Thus, we can develop a method of hand tracking for gesture and behavior recognition techniques frequently used in conjunction with human-robot interaction (HRI) components. The experimental results show that the proposed method yields a good performance in the intelligent service robots, so called Wever developed in ETRI

The user authentication problem is discussed in the context of robot environments. Although authentication is indispensable for useful robot services, the traditional approaches that have been applied in strictly regulated environments cannot be applied as they are when both the robot and humans are freely moving around. In this paper, a new authentication system redesigned to be adapted to robot environments is described. In the proposed system, semi-biometrics and user tracking are incorporated to cope with the limited applicability of traditional authentication techniques. Semi-biometrics indicates a set of features useful for discriminating persons, but only in interested group of persons and in interested frame of time. As a representative semi-biometric feature, body height and color characteristics of the clothes are investigated. Especially, a novel method to measure body height with single camera is proposed. Also by incorporating tracking functionality, the system could maintain the user status information continuously, which is useful not only for recognition but also for finding a designated person.