• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제1권1호
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• pp.1-8
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• 2020

Recent studies revealed that many animals identify individual humans. In this account, we review previous literatures on individual human recognition by wild or domestic animals and discuss the three hypotheses: "high cognitive abilities" hypothesis, "close human contact" and "pre-exposure to stimuli" hypothesis. The three hypotheses are not mutually exclusive. Close human contact hypothesis is an ultimate explanation for adaptive benefits whereas high cognitive abilities and pre-exposure to stimuli hypothesis are proximate explanations for mechanisms to perform such discriminatory behaviour. We report a case study of two bird species in a human-free habitat. Long-tailed skuas, which are known for having high cognitive abilities, exhibited the human discriminatory abilities whereas ruddy turnstones did not display such abilities toward approaching humans. This suggests that highly intelligent species may have this type of discriminatory ability so that they could learn to identify individual humans quickly by pre-exposure to stimuli, even in a human-free habitat. Here, we discuss that human recognition is more common in species with rapid learning ability and it could develop for a short period of time between an intelligent species and human.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1473-1480
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• 2018

The extraction and recognition of human motion characteristics need to combine biometrics to determine and judge human behavior in the movement and distinguish individual identities. The so-called biometric technology, the specific operation is the use of the body's inherent biological characteristics of individual identity authentication, the most noteworthy feature is the invariance and uniqueness. In the past, the behavior recognition technology based on the single characteristic was too restrictive, in this paper, we proposed a mixed feature which combined global silhouette feature and local optical flow feature, and this combined representation was used for human action recognition. And we will use the KTH database to train and test the recognition system. Experiments have been very desirable results.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.1097-1100
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• 2005

개체명 인식에 있어서 반드시 선행되어야 할 작업이 문서의 내용을 대표하는 용어의 추출이다. 높은 신뢰도의 개체명 인식은 정보추출 시스템구축을 한 차원 높일 수 있을 것이다. 지금까지 일반적인 개체명 인식이나 인명의 개체명 인식에 대한 많은 연구가 활발하게 진행되어 왔지만 세분화된 지명 인식의 연구는 다루어지지 않았다. 본 논문에서는 수작업으로 작성된 규칙을 적용하여 세분화한 사전기반의 한국어 지명인식 시스템 개발 방법을 제안한다.

• 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.

• 융합신호처리학회논문지
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• 제12권4호
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• pp.228-236
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• 2011

Emotion recognition plays an important role in the research area of human-computer interaction, and it allows a more natural and more human-like communication between humans and computer. Most of previous work on emotion recognition focused on extracting emotions from face, speech or EEG information separately. Therefore, a novel approach is presented in this paper, including face, speech and EEG, to recognize the human emotion. The individual matching scores obtained from face, speech, and EEG are combined using a weighted-summation operation, and the fused-score is utilized to classify the human emotion. In the experiment results, the proposed approach gives an improvement of more than 18.64% when compared to the most successful unimodal approach, and also provides better performance compared to approaches integrating two modalities each other. From these results, we confirmed that the proposed approach achieved a significant performance improvement and the proposed method was very effective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• 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.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• 한국생활과학회지
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• 제20권5호
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• pp.993-1008
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• 2011

An increase in interests in consumers' social responsibilities, or ethical spending, starts from a recognition that the consumption of an individual does not stop with the individual, but also affects overall society. The recognition of consumers' social responsibilities leads to demands for corporate social responsibility. Therefore, this study analyzed how social responsibility recognition affects consumers' needs for corporate social responsibility using college students. All data was analyzed with the SPSS Windows 18.0 program in terms of frequency, Crobach's ${\alpha}$, factor analyses, paired t-test, one-way ANOVA, and multiple regression. The results are as following: first, the recognition level of consumers' social responsibilities in college students was at an average level and the consumer's needs for corporate social responsibility were higher than usual. Second, the grade level, military experience, and economic status of the college students changed their views on consumers' needs for corporate social responsibility. Groups with higher consumers' social responsibilities had higher consumer demands for corporate social responsibility. Through this, we can see that consumers' social responsibilities affects the consumer's needs for corporate social responsibility.

• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1676-1687
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• 2008

In this paper, we proposed a new multi-view human recognition method based on face and gait features detection algorithm. For getting the position of moving object, we used the different of two consecutive frames. And then, base on the extracted object, the first important characteristic, walking direction, will be determined by using the contour of head and shoulder region. If this individual appears in camera with frontal direction, we will use the face features for recognition. The face detection technique is based on the combination of skin color and Haar-like feature whereas eigen-images and PCA are used in the recognition stage. In the other case, if the walking direction is frontal view, gait features will be used. To evaluate the effect of this proposed and compare with another method, we also present some simulation results which are performed in indoor and outdoor environment. Experimental result shows that the proposed algorithm has better recognition efficiency than the conventional sing]e view recognition method.