• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• IEIE Transactions on Smart Processing and Computing
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• 제1권1호
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• pp.1-7
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• 2012

This paper presents a system used to automatically recognize the road traffic control gestures of police officers. In this approach,the control gestures of traffic police officers are captured in the form of depth images.A human skeleton is then constructed using a kinematic model. The feature vector describing a traffic control gesture is built from the relative angles found amongst the joints of the constructed human skeleton. We utilize Support Vector Machines (SVMs) to perform the gesture recognition. Experiments show that our proposed method is robust and efficient and is suitable for real-time application. We also present a testbed system based on the SVMs trained data for real-time traffic gesture recognition.

• 로봇학회논문지
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• 제1권2호
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• pp.158-162
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• 2006

This paper presents a speaker recognition system intended for use in human-robot interaction. The proposed speaker recognition system can achieve significantly high performance in the Ubiquitous Robot Companion (URC) environment. The URC concept is a scenario in which a robot is connected to a server through a broadband connection allowing functions to be performed on the server side, thereby minimizing the stand-alone function significantly and reducing the robot client cost. Instead of giving a robot (client) on-board cognitive capabilities, the sensing and processing work are outsourced to a central computer (server) connected to the high-speed Internet, with only the moving capability provided by the robot. Our aim is to enhance human-robot interaction by increasing the performance of speaker recognition with multiple microphones on the robot side in adverse distant-talking environments. Our speaker recognizer provides the URC project with a basic interface for human-robot interaction.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• 한국생활과학회지
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• 제22권5호
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• pp.407-417
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• 2013

The purpose of this study was to investigate and analyze how teachers' recognition of professionalism and the child's gender and term care affect child's social interaction behavior. Participants were three-year-old 61 children and their 20 teachers. Each child was observed by the time sampling method of 20 sec-observation followed by 10 sec-recording for a total of 14 minutes. The teachers completed the rating scales to measure the teachers' recognition of professionalism. The study results show that, children engaged more frequently in individual behavior than in interactions with peers or with teachers in day care centers. And those children had more interaction behavior with their teachers than with their peers. Correlation between teachers' recognition of professionalism and children's social interaction behavior were as following: the more the teachers recognized professionalism, the more the children showed positive interaction behavior toward their teachers. Also, the more the teachers recognized the professionalism related to the job satisfaction, the more the children showed positive interaction behavior toward their peers. Boys interacted more negatively with peers and teachers than girls did. Children who attended the day care center more than two years showed less individual behaviors than others.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.129-138
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• 2022

With the increasing reliance of computing systems in our everyday life, there is always a constant need to improve the ways users can interact with such systems in a more natural, effective, and convenient way. In the initial computing revolution, the interaction between the humans and machines have been limited. The machines were not necessarily meant to be intelligent. This begged for the need to develop systems that could automatically identify and interpret our actions. Automatic gesture recognition is one of the popular methods users can control systems with their gestures. This includes various kinds of tracking including the whole body, hands, head, face, etc. We also touch upon a different line of work including Brain-Computer Interface (BCI), Electromyography (EMG) as potential additions to the gesture recognition regime. In this work, we present an overview of several applications of automated gesture recognition systems and a brief look at the popular methods employed.

• 제어로봇시스템학회논문지
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• 제14권2호
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• pp.178-183
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• 2008

Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• 디지털산업정보학회논문지
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• 제8권1호
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• pp.29-40
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• 2012

A challenging research issue that has been one of growing importance to those working in human-computer interaction are to endow a machine with an emotional intelligence. Thus, emotion recognition technology plays an important role in the research area of human-computer interaction, and it allows a more natural and more human-like communication between human and computer. In this paper, we propose the multimodal emotion recognition system using face and speech to improve recognition performance. The distance measurement of the face-based emotion recognition is calculated by 2D-PCA of MCS-LBP image and nearest neighbor classifier, and also the likelihood measurement is obtained by Gaussian mixture model algorithm based on pitch and mel-frequency cepstral coefficient features in speech-based emotion recognition. The individual matching scores obtained from face and speech are combined using a weighted-summation operation, and the fused-score is utilized to classify the human emotion. Through experimental results, the proposed method exhibits improved recognition accuracy of about 11.25% to 19.75% when compared to the most uni-modal approach. From these results, we confirmed that the proposed approach achieved a significant performance improvement and the proposed method was very effective.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.186-192
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• 2009

본 논문은 비전을 이용한 인간 정면 상반신 포즈를 인식 방법에 대해서 기술한다. 일반적으로 HCI(Human Computer Interaction)와 HRI(Human Robot Interaction)에서는 인간이 정면을 바라볼 때 얼굴, 손짓으로 의사소통 하는 경우가 많기 때문에 본 논문에서는 인식의 범위를 인간의 정면 그리고 상반신에 대해서만 한정한다. 인간 포즈인식의 주요 두 가지 어려움은 첫째 인간은 다양한 관절로 이루어진 객체이기 때문에 포즈의 자유도가 높은 문제점 때문에 모델링이 어렵다는 것이다. 둘째는 모델링된 정보와 영상과의 매칭이 어려운 것이다. 이를 해결하기 위해 본 논문에서는 모델링이 쉬운 그림모델(Pictorial Model)을 이용해 인체를 다수 사각형 파트로 모델링 하였고 이를 이용해 주요한 상반신 포즈를 DB화 해 인식한다. DB 포즈로 표현되지 못하는 세부포즈는 인식된 주요 포즈 파라미터로 부터 파티클필터를 이용해 예측한 다수 파티클로부터 가장 높은 사후분포를 갖는 파티클을 찾아 주요 포즈를 업데이트하여 결정한다. 따라서 주요한 포즈 인식과 이를 기반으로 한 세부 포즈를 추적하는 두 단계를 통해 인체 정면 상반신 포즈를 정확하게 인식 할 수 있다.

• 제어로봇시스템학회논문지
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• 제19권8호
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• pp.738-744
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• 2013

According to the cognitive science research, the interaction intent of humans can be estimated through an analysis of the representing behaviors. This paper proposes a novel methodology for reliable intention analysis of humans by applying this approach. To identify the intention, 8 behavioral features are extracted from the 4 characteristics in human-human interaction and we outline a set of core components for nonverbal behavior of humans. These nonverbal behaviors are associated with various recognition modules including multimodal sensors which have each modality with localizing sound source of the speaker in the audition part, recognizing frontal face and facial expression in the vision part, and estimating human trajectories, body pose and leaning, and hand gesture in the spatial part. As a post-processing step, temporal confidential reasoning is utilized to improve the recognition performance and integrated human model is utilized to quantitatively classify the intention from multi-dimensional cues by applying the weight factor. Thus, interactive robots can make informed engagement decision to effectively interact with multiple persons. Experimental results show that the proposed scheme works successfully between human users and a robot in human-robot interaction.