• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.17-19
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• 2021

본 논문에서는 코로나19 사태에 대비하여 실시간으로 마스크를 제대로 쓴 사람과 제대로 쓰지 않은 사람을 구분하는 시스템을 제안한다. 이 시스템을 사용하기 위하여 모델 학습 시에 합성곱 신경망(CNN : Convolutional Neural Networks)를 사용한다. 학습된 모델을 토대로 영상에 적용 시 하르 특징 분류기(Haar Cascade Classifier)로 얼굴을 탐지하여 마스크 여부를 판단한다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.588-594
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• 2021

Due to the recent proliferation of video contents, previous contents expressed as the character or the picture are being replaced to video and growth of video contents is being boosted because of emerging new platforms. As this accelerated growth has a great impact on the process of universalization of technology for ordinary people, video production and editing technologies that were classified as expert's areas can be easily accessed and used from ordinary people. Due to the development of these technologies, tasks like that recording and adjusting that depends on human's manual involvement could be automated through object tracking technology. Also, the process for situating the object in the center of the screen after finding the object to record could have been automated. Because the task of setting the object to be tracked is still remaining as human's responsibility, the delay or mistake can be made in the process of setting the object which has to be tracked through a human. Therefore, we propose a novel object tracking technique of CMT combining the face detection technique utilizing Haar cascade classifier. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the smartphone in real time.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.763-765
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• 2005

본 논문에서는 점증적 분류 성능을 갖는 단계형(cascade) 분류기를 이용한 새로운 실시간 얼굴 탐지시스템을 제안하고자 한다. 제안된 시스템의 첫 단계는 전처리 단계로써 매우 빠른 속도를 갖는 새로운 피부색 탐지기를 이용하여 탐색 공간을 대폭 축소하고, 두 번째 단계에서는 빠른 분류가 가능한 유사-하(Haar-like) 특징을 이용한 단계형 분류기를 배치하여 빠른 속도로 후보 얼굴을 검출한다. 마지막 단계에서는 탐지율을 높이기 위해 단일 클래스 SVM인 SVDD를 분류기로 사용하였으며, 실험을 통하여 제안된 시스템의 우수성을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1022-1024
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• 2015

휴먼-컴퓨터 인터렉션과 같은 응용분야에서 손동작 인식을 위한 많은 연구가 이루어지고 있다. 손동작 인식을 이용한 인터렉션의 성능 향상을 위해서는 정확한 손 영역 검출이 필요하다. 본 논문에서는 색상 정보를 이용하여 Cascade 방식에 기반한 손 영역 검출 방법을 제안한다. Cascade 방식으로 손 영역을 검출할 경우보다 강인한 인식률을 얻기 위해서 색상정보를 이용하였으며, Haar-like 특징점으로 학습된 분류기를 통해 손 영역 검출 방법을 제안한다.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.243-248
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• 2013

This paper proposes an effective method using Adaptive Boosting to track a person's head in complex background. By only one way to feature extraction methods are not sufficient for modeling a person's head. Therefore, the method proposed in this paper, several feature extraction methods for the accuracy of the detection head running at the same time. Feature Extraction for the imaging of the head was extracted using sub-region and Haar wavelet transform. Sub-region represents the local characteristics of the head, Haar wavelet transform can indicate the frequency characteristics of face. Therefore, if we use them to extract the features of face, effective modeling is possible. In the proposed method to track down the man's head from the input video in real time, we ues the results after learning Harr-wavelet characteristics of the three types using AdaBoosting algorithm. Originally the AdaBoosting algorithm, there is a very long learning time, if learning data was changes, and then it is need to be performed learning again. In order to overcome this shortcoming, in this research propose efficient method using cascade AdaBoosting. This method reduces the learning time for the imaging of the head, and can respond effectively to changes in the learning data. The proposed method generated classifier with excellent performance using less learning time and learning data. In addition, this method accurately detect and track head of person from a variety of head data in real-time video images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.143-149
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• 2019

Floating holograms are technologies that provide rich 3D stereoscopic images in a wide space such as advertisement, concert. In addition, It is possible to reduce the 3D glasses inconvenience, eye strain, and space distortion, and to enjoy 3D images with excellent realism and existence. Therefore, this paper implements a user gesture recognition system for manipulating a floating hologram characters that can be used in a small space devices. The proposed method detects face region using haar feature-based cascade classifier, and recognizes the user gestures using a user gesture-occurred position information that is acquired from the gesture difference image in real time. And Each classified gesture information is mapped to the character motion in floating hologram for manipulating a character action. In order to evaluate the performance of the proposed user gesture recognition system for manipulating a floating hologram character, we make the floating hologram display devise, and measures the recognition rate of each gesture repeatedly that includes body shaking, walking, hand shaking, and jumping. As a results, the average recognition rate was 88%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1334-1339
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• 2018

Viola & Jones's face detection algorithm is a typical face detection algorithm and shows excellent face detection performance. However, the Viola & Jones's algorithm in images including many faces generates undetected faces and wrong detected faces, such as false faces and duplicate detected faces, due to face diversity. This paper proposes an improved face detection algorithm using a face verification algorithm that eliminates the false detected faces generated from the Viola & Jones's algorithm. The proposed face verification algorithm verifies whether the detected face is valid by evaluating its size, its skin color in the designated area, its edges generated from eyes and mouth, and its duplicate detection. In the face verification experiment of 658 face images detected by the Viola & Jones's algorithm, the proposed face verification algorithm shows that all the face images created in the real person are verified.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.859-864
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• 2017

IoT is a new emerging technology to lead the $4^{th}$ industry renovation and has been widely used in industry and home to increase the quality of human being. In this paper, IoT based face detection and recognition system for a smart elevator is developed. Haar cascade classifier is used in a face detection system and a proposed PCA algorithm written in Python in the face recognition system is implemented to reduce the execution time and calculates the eigenfaces. SVM or Euclidean metric is used to recognize the faces detected in the face detection system. The proposed system runs on RaspberryPi 3. 200 sample images in ORL face database are used for training and 200 samples for testing. The simulation results show that the recognition rate is over 93% for PP+EU and over 96% for PP+SVM. The execution times of the proposed PCA and the conventional PCA are 0.11sec and 1.1sec respectively, so the proposed PCA is much faster than the conventional one. The proposed system can be suitable for an elevator monitoring system, real time home security system, etc.