• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.106-109
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• 2003

Many diverse methods have been developing in the field of biometric identification as human-friendliness has been emphasized in the intelligent system's area. One of emerging method is to use human footprint. Automated footprint-based person recognition was started by Nakajima et al.'s research but they showed relatively low recognition result by low spatial resolution of pressure sensor and standing posture. In this paper, we proposed a modified Nakajima's method to use walking footprint which could give more stable toe information than standing posture. Finally, we prove the usefulness of proposed method as 91.4tt recognition rate in 11 volunteers' test.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.821-823
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• 1999

This paper describes a study on a person identification system which can improve currently available biometric systems. In the procedure of PIN(Personal Identification Number) input, holding time, interkey time between key presses are measured and normalized. Person identification is performed by matching using Euclidean distance of these punching dynamics. The experimental results show the possibility of improvement of the overall system performance when keypad dynamics feature is applied to the biometric systems which take PIN input using keypads.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1114-1135
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• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• Land and Housing Review
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• v.11 no.2
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• pp.1-14
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• 2020

The aim of this study is to empirically identify the differentiating characteristics of determinant factors on sing-person households' commuting mode choice compared to multi-person households' one in order to establish the customized police directions to decrease private car use in commuting. While the study use the 2% sample survey data on the population and housing in 2015, it employ multinomial logit models on relative choice probability of such alternative commuting modes as bus, subway or rail, and walking, rather than driving. As potential determinant factors, the study employs demographic, socio-economic, and housing and residential one for both models of single-person and multi-person households. The study finds that the behavior of commuting mode choice has distinctive difference by gender, marriage status, physical activity constraint, job type, residential period in current housing of the single-person household's workers compared to the multi-person households' ones. Based on the findings, the study deduce ten commuting policy directions customized for the single-person household.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.201-212
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• 2011

This study examines the potentials of realistic controller and point-of-view to affect users' arousal, valence, identification and engagement. While media researchers have advocated the role of realistic controller and point-of-view as a possible contributor to psychological reactions of playing video game, this claim is based on a relatively small number of empirical studies. Collegiate subjects took part in an experimental investigation manipulation the level of controller realism(gamepad vs. steering wheel) and point-of-view(first person vs. third person). Results of the study showed the influence of controller realism and point-of-view on arousal, identification and engagement, and the implication of the findings are discussed.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• MALSORI
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• no.62
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• pp.149-161
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• 2007

In this paper we proposed a modified reliability function for improving bimodal speaker identification(BSI) performance. The convectional reliability function, used by N. Fox[1], is extended by introducing an optimization factor. We evaluated the proposed method in BSI domain. A BSI system was implemented based on GMM and it was tested using VidTIMIT database. Through speaker identification experiments we verified the usefulness of our proposed method. The experiments showed the improved performance, i.e., the reduction of error rate by 39%.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.134-136
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• 2003

This paper proposes an algorithm for fingerprint identification using phase only correlation. This algorithm uses the phase of fast Fourier transform and correlation function to calculate the similarity. The algorithm gives very clear result for identification because it shows only one conspicuous sharp peak for the same person's fingerprint. Further, it shows good results even for the finger print images which are printed not clearly and does not need to preprocess the images. It also shows good results for parallel displacement of fingerprint. The experiment result shows the effectiveness of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.19-25
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• 2021

In this paper proposes a method of finding missing persons based on face-recognition technology and deep learning. In this paper, a real-time face-recognition technology was developed, which performs face verification and improves the accuracy of face identification through data fortification for face recognition and convolutional neural network(CNN)-based image learning after the pre-processing of images transmitted from a mobile device. In identifying a missing person's image using the system implemented in this paper, the model that learned both original and blur-processed data performed the best. Further, a model using the pre-learned Noisy Student outperformed the one not using the same, but it has had a limitation of producing high levels of deflection and dispersion.