• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.583-585
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• 2012

본 논문은 웹카메라를 이용하여 얼굴이 바라보고 있는 방향을 인식하는 시스템을 제안한다. 얼굴 검출 방법으로 Haar-like Face Detect를 이용하여 얼굴을 검출하고 전체 이미지에서 검출된 얼굴 영역만을 관심영역으로 설정하여 Haar-like Eye Detect를 이용하여 눈 영역을 검출하였다. 검출된 눈 위치에 대한 평균값으로 얼굴이 왼쪽 방향을 보고 있는지 오른쪽 방향을 보고 있는지를 판단하였다. 제안된 방법의 실험 결과, 얼굴 및 눈 영역을 비교적 정확하게 검출하였으며 계산된 눈 위치를 이용하여 얼굴 방향 인식에 대해서 우수한 성능을 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1184-1192
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• 2004

A real-time pupil detection system that detects the pupil movement from the real-time video data achieved by the visual light camera for general purpose personal computer is proposed. It is implemented with three steps; at first, face region is detected using the Haar-like feature detection scheme, and then eye region is detected within the face region using the template-based scheme. Finally, pupil movement is detected within the eye region by convolution of the horizontal and vertical histogram profiling and Gaussian filter. As results, we obtained more than 90% of the detection rate from 2375 simulation images and the data processing time is about 160㎳, that detects 7 times per second.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.6
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• pp.259-265
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• 2001

In the last two decades, many researchers proposed various usages of the orthogonal functions such as Walsh, Haar and BPF to solve the system analysis, optimal control, and identification problems from and algebraic form. In this paper, a simple procedure to design and algerbraic observer for the descriptor system is presented by using block pulse function expansions. The main characteristic of this technique is that it converts differential observer equation into an algerbraic equation. And furthermore, a simple recursive algorithm is proposed to obtain BPFs coefficients of the observer equation.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.939-946
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• 2005

The stiffness properties of heterogeneous concrete materials and their degradation were investigated at different-levels of observations with aids of the opportunities and limitations of multi-resolution wavelet analysis. The successive Haw transformations lead to a recursive separation of the stiffness properties and the response into coarse-and fine-scale features. In the limit, this recursive process results in a homogenization parameter which is an average measure of stiffness and strain energy capacity at the coarse scale. The basic concept of multi-resolution analysis is illustrated with one and two-dimensional model problems of a two-phase particulate composite representative of the morphology of concrete materials. The computational studies include the meso-structural features of concrete in the form of a hi-material system of aggregate particles which are immersed in a hardened cement paste taking due to account of the mismatch of the two elastic constituents.

• Smart Media Journal
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• v.11 no.2
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• pp.25-30
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• 2022

With the recent development of device performance such as smartphones, cameras, and video cameras, it has become possible to obtain human biometric information from images and photos. A German hacker group obtained human iris information from high-definition photos and revealed hacking into iris scanners on smartphones. As high-quality images and photos can be obtained with such advanced devices, the need for a suitable security system is also emerging. Therefore, in this paper, we propose a method of automatically masking human iris information in images and photos using Haar Cascades and Blur models from openCV. It is a technology that automatically masks iris information by recognizing a person's eye in a photo or video and provides the result. If this technology is used in devices and applications such as smartphones and zoom, it is expected to provide better security services to users.

• East Asian mathematical journal
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• v.35 no.3
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• pp.319-329
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• 2019

In this paper, we provide sufficient conditions of a pair of weights (u, v) and a function b so that the dyadic paraproduct is bounded from $L^2_u(X)$ into $L^2_v(X)$, where X is a space of homegeneous type. In order to prove the main result we use the honest dyadic system introduced in [10].

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.26-35
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• 2007

We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.47-55
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• 2010

In this paper, we proposed a recognition system of smile facial expression for smile treatment training. The proposed system detects face candidate regions by using Haar-like features from camera images. After that, it verifies if the detected face candidate region is a face or non-face by using SVM(Support Vector Machine) classification. For the detected face image, it applies illumination normalization based on histogram matching in order to minimize the effect of illumination change. In the facial expression recognition step, it computes facial feature vector by using PCA(Principal Component Analysis) and recognizes smile expression by using a multilayer perceptron artificial network. The proposed system let the user train smile expression by recognizing the user's smile expression in real-time and displaying the amount of smile expression. Experimental result show that the proposed system improve the correct recognition rate by using face region verification based on SVM and using illumination normalization based on histogram matching.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.174-179
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• 2018

Currently, various security technologies such as fingerprint recognition and face recognition are being developed. However, although many technologies have been developed, the field of incorporating technologies is quite limited. In particular, it is easy to adapt modern security technologies into existing digital systems, but it is difficult to introduce new digital technologies in systems using analog systems. However, if the system can be widely used, it is worth replacing the analog system with the digital system. Therefore, the selected topic is the electronic attendance system. In this paper, a camera is installed to a door to perform a Haar-like feature training for face detecting and real-time face recognition with a Eigenface in principal component analysis(PCA) based face recognition using raspberry pi. The collected data was transmitted to the smartphone using wireless communication, and the application for the viewer who can receive and manage the information on the smartphone was completed.