• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.31-40
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• 2006

In this paper, we propose a gait recognition method based on gait silhouette sequences. Features of gait are affected by the variation of gait direction. Therefore, we synthesize silhouettes to canonical form by using planar homography in order to reduce the effect of the variation of gait direction. The planar homography is estimated with only the information which exist within the gait sequences without complicate operations such as camera calibration. Even though gait silhouettes are generated from an individual person, fragments beyond common characteristics exist because of errors caused by inaccuracy of background subtraction algorithm. In this paper, we use the Principal Component Analysis to analyze the deviated characteristics of each individual person. PCA used in this paper, however, is not same as the traditional strategy used in pattern classification. We use PCA as a criterion to analyze the amount of deviation from common characteristic. Experimental results show that the proposed method is robust to the variation of gait direction and improves separability of test-data groups.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.23-30
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• 2005

As a human identification technique, gait recognition has recently gained significant attention. Silhouette-based gait recognition is one of the most popular methods. This paper aims to investigate features that determine the style of walking in silhouette-based gait recognition. Gait can be represented using two cues: static(shape) cue and dynamic(motion) cue. Most recently, research results have been reported in the literature that the characteristics of gait are mainly determined by static cue but not affected by dynamic cue. Unlike this, experimental results in this paper verifies that dynamic cue is as important as and in many cases more important than static cue. For experiments, we use two well-blown gait databases: UBC DB and Southampton Small DB. The images of UBC DB correspond to the 'ordinary' style of walking. The images of Southampton Small DB correspond to the 'disguised' (not ordinary by wearing special clothes or bags) style of walking. As results of experiments, the recognition rate was 100% by static cue and $95.2\%$ by dynamic cue for the images of UBC DB. For the images of Southampton Small DB, the recognition rate was $50.0\%$ by static cue and $55.8\%$ by dynamic cue. The risk against correct recognition was 0.91 by static cue and 0.97 by dynamic cue for the images of UBC DB. For the images of Southampton Small DB, the risk was 0.98 by static cue and 0.98 by dynamic cue. Consequently, the characteristics of ordinary gait are mainly determined by static cue but that of disguised gait by dynamic cue.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.6-11
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• 2011

In this paper, we propose a gait-based human identification system using eigenfeature regularization and extraction (ERE). First, a gait feature for human identification which is called gait energy image (GEI) is generated from walking sequences acquired from a camera sensor. In training phase, regularized transformation matrix is obtained by applying ERE to the gallery GEI dataset, and the gallery GEI dataset is projected onto the eigenspace to obtain galley features. In testing phase, the probe GEI dataset is projected onto the eigenspace created in training phase and determine the identity by using a nearest neighbor classifier. Experiments are carried out on the CASIA gait dataset A to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than previous works in terms of correct classification rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1405-1417
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• 2017

Researches that apply the acceleration signal due to user's gait measured at the wearable device to the authentication technology are being introduced recently. The gait acceleration signal based authentication technologies introduced so far have assumed that an attacker can obtain a user's gait acceleration signal only by attaching accelerometer directly to user's body. And the practical attack method for gait acceleration signal based authentication technology is mimic attack and it uses a person whose physical condition is similar to the victim or identifies the gait characteristics through the video of the gait of the victim. However, mimic attack is not effective and attack success rate is also very low, so it is not considered a serious threat. In this paper, we propose Video Gait attack as a new attack method for gait acceleration signal based authentication technology. It is possible to know the position of the wearable device from the user's gait video signal and generate a signal that is very similar to the accelerometer's signal using dynamic equation. We compare the user's gait acceleration signal and the signal that is calculated from video of user's gait and dynamic equation with experiment data collected from eight subjects.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1407-1412
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• 2013

The human gait pattern analysis shoes have been developed for our healthy lfe, which is largely dependent on a posture and a skeletal structure affected by daily lifestyle and gait pattern. There are generally 3 types of human gait, such as normal gait, intoeing gait, and outtoeing gait. We have analyzed one's gait pattern through walking put on the developed shoes.

• Annual Conference of KIPS
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• 2018.10a
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• pp.645-647
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• 2018

최근 IoT의 기술의 발달로 사용자 인식에 관한 연구가 주목을 받고 있다. 사용자 인식은 각 사용자만의 특징에 근거하여 특정 사용자를 인식하는 기술이다. 사용자 인식과 관련하여 홍채나 지문인식 등과 같은 생체 인식, 얼굴 인식 그리고 걸음걸이 인식 등에 관한 연구들이 진행되고 있다. 다양한 방식은 각각의 인식률을 높이기 위해 노력하고 있지만, 인식하고자 하는 사용자의 상황에 따라 인식률에 영향을 받게 된다. 본 연구에서는 다양한 방식을 여러 단계로 구성하여 다양한 상황에 놓인 사용자를 인식하기 위한 방법을 연구한다. 제안 시스템은 드론에서 촬영된 영상을 수신하는 것을 기반으로 하여 얼굴인식과 걸음걸이 인식을 이용한 방식이다. 1차적으로 사람의 얼굴을 탐지를 하고, 사람의 얼굴이 탐지되었을 때는 얼굴 인식을 수행한다. 탐지하지 못했을 경우 걸음걸이 인식을 수행하여 인식률을 향상시킨다.

• Proceedings of the Costume Culture Conference
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• 2003.09a
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• pp.130-131
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• 2003

인간은 자연계에서 두 발로 걷는 유일한 존재로, 발은 사람의 표정과 마찬가지로 상황, 사람, 감정 등에 반응하여, 분위기나 느낌을 무의식적이지만 적극적으로 표현한다. 우아하고 품위 있는 걸음걸이는 성적 매력과 에로틱한 감정을 불러 일으키는 핵심이 되며, 이것은 특히 여성에게 적용된다. 인간뿐 아니라 다른 많은 동물들에게서도 걸음걸이는 구애나 짝짓기를 하는 기간동안 과장된 성적 매력을 발산하는데, 이는 성적인 신호뿐 아니라 성적 흥분 그 자체에도 기여한다. (중략)

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.354-362
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• 2010

This paper proposes a new method for a hierarchical analysis of human gait by dividing the motion into gait direction and gait posture using the tool of dynamic Bayesian network. Based on Factorial HMM (FHMM), which is a type of DBN, we design the Gait Motion Decoder (GMD) in a circular architecture of state space, which fits nicely to human walking behavior. Most previous studies focused on human identification and were limited in certain viewing angles and forwent modeling of the walking action. But this work makes an explicit and separate modeling of pedestrian pose and posture to recognize gait direction and detect orientation change. Experimental results showed 96.5% in pose identification. The work is among the first efforts to analyze gait motions into gait pose and gait posture, and it could be applied to a broad class of human activities in a number of situations.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.622-627
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• 2014

This paper proposes a new method which improves recognition rate on the gait recognition system using wavelet transform, linear discriminant analysis and genetic algorithm. We use wavelet transform to obtain the four sub-bands from the gait energy image. In order to extract feature data from sub-bands, we use linear discriminant analysis. Distance values between training data and four sub-band data are calculated and four weights which are calculated by genetic algorithm is assigned at each sub-band distance. Based on a new fusion distance value, we conducted recognition experiments using k-nearest neighbors algorithm. Experimental results show that the proposed weight fusion method has higher recognition rate than conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1901-1906
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• 2010

This classification of walking patterns is important and many kinds of applications. Therefore, we attempted to classify walking on level ground from slow walking to fast walking using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by bluetooth module at a sampling rate of 100 Hz eleven healthy. The data were analyzed using discrete wavelet transform. Walking patterns were classified using two parameters; One was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction(RAV). Slow walking could be distinguished by the smallest value in RPA from other walking pattern. Fast walking could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signal in healthy people.