• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.301-303
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• 2006

본 논문은 중립 표정과 같은 표정 측정의 기준이 되는 단서 없이 다양한 내적상태 안에서 얼굴표정을 인식할 수 있는 개선된 시스템을 제안한다. 표정정보를 추출하기 위한 전처리작업으로, 백색화(whitening) 단계가 적용되었다. 백색화 단계는 영상데이터들의 평균값이 0이며, 단위분산값으로 균일한 분포를 갖도록 하여 조명 변화에 대한 민감도를 줄인다. 백색화 단계 수행 후 제 1 주성분이 제외된 나머지 주성분들로 이루어진 PCA표상을 표정정보로 사용함으로써 중립 표정에 대한 단서 없이 얼굴표정의 특징추출을 가능하게 하였다. 본 실험 결과는 83개의 내적상태와 일치되는 다양한 얼굴표정들에서 임의로 선택된 표정영상들의 얼굴표정 인식을 수행함으로써 다양하고 자연스런 얼굴 표정인식을 가능하게 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.139-142
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• 2001

컴퓨터 이미지처리는 여러 분야에서 응용되고 있는데 어떤 특성을 만족하는 객체들의 계수를 자동으로 분류시키는 생물학분야, 편지봉투나 일반양식에 인쇄되어 있는 글자를 자동으로 검출하고 인식하며 초음파검사 혹은 X-Ray 촬영에서 이미지를 획득하여 향상시키는 의료분야, 지문 및 얼굴인식 등에 이용되고 있다. 최근 몇 년 동안 이미지인식, 형태론, 이미지데이터 압축에 관한 연구가 진전되면서 본 연구에서 형태론적인 기법을 사용하여 문자인식을 위한 전처리 혹은 후처리 단계에서 사용되는 이미지향상을 위해서 침식, 골격화의 2단계를 적용하여 기종의 연구 방법과 비교하여 이미지획득 시간을 줄이고 이미지를 향상시켜 문자를 인식하는 알고리즘을 제안한다.

• Journal of KIISE
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• v.44 no.3
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• pp.295-305
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• 2017

Facial appearance is greatly influenced by illumination conditions, and therefore illumination variation is one of the factors that degrades performance of face recognition systems. In this paper, we propose a robust method for face representation under varying illumination conditions, combining non-alpha Weberface (non-alpha WF) and histogram equalization. We propose a two-step method: (1) for a given face image, non-alpha WF, which is not applied a parameter for adjusting the intensity difference between neighboring pixels in WF, is computed; (2) histogram equalization is performed to non-alpha WF, to make a uniform histogram distribution globally and to enhance the contrast. $(2D)^2PCA$ is applied to extract low-dimensional discriminating features from the preprocessed face image. Experimental results on the extended Yale B face database and the CMU PIE face database show that the proposed method yielded better recognition rates than several illumination processing methods as well as the conventional WF, achieving average recognition rates of 93.31% and 97.25%, respectively.

• Korean Journal of Cognitive Science
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• v.8 no.4
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• pp.55-62
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• 1997

We proposed a method for locating of mouth using deformable templates, described by a parameterized template. An energy function is defined which links, edges, peaks, valleys in image intensity to corresponding properties of the template. The template deforms itself by altering its parameter values to minimize the energy function. The minimized energy function's parameter values can be used as descriptors for the feature. We propose a method for locating mouth fast, accurately by limiting a range of parameters' value and getting initial value of parameters' by preprocessing.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.821-829
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• 2004

A face recognition has been used for verifying and authorizing valid users, but its applications have been restricted according to lighting conditions. In order to minimizing the restricted conditions, this paper proposes a new algorithm of detecting the face from the input image obtained under the irregular lighting condition. First, the proposed algorithm extracts an edge difference image from the input image where a skin color and a face contour are disappeared due to the background color or the lighting direction. In the next step, it extracts a face region using the histogram of the edge difference image and the intensity information. Using the intensity information, the face region is divided into the horizontal regions with feasible facial features. The each of horizontal regions is classified as three groups with the facial features(including eye, nose, and mouth) and the facial features are extracted using empirical properties of the facial features. Only when the facial features satisfy their topological rules, the face region is considered as a face. It has been proved by the experiments that the proposed algorithm can detect faces even when the large portion of face contour is lost due to the inadequate lighting condition or the image background color is similar to the skin color.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.109-116
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation for the identification of a face shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the face area through pre-processing using a face shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a face area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the face shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.16-18
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• 2017

최근 딥 러닝의 발전으로 얼굴인식뿐만 아니라 더 세부적인 기술인 ID식별, 감정인식 등을 분류할 수 있는 알고리즘이 많이 제안되었다. 하지만 딥 러닝은 방대한 연산량을 처리해야 하기 때문에 실시간으로 영상을 구현하는 것은 한계가 있다. 본 논문은 위와 같은 문제를 개선하기 위하여 얼굴인식은 연산량이 비교적 적은 HOG알고리즘을 적용하여 전처리를 진행한다. 그 이후 ID식별 네트워크인 FaceNet과 EmotiW 2017 Challenge의 논문의 감정인식 네트워크를 Multi-Thread 기술을 적용하여 스레드를 분할 연산을 통하여 실시간으로 영상을 출력하는 알고리즘을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.451-454
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• 2005

본 논문에서는 수정된 CNN(Convolutional Neural Network) 모델과 다중 필터가 상호 결합된 형태의 얼굴 패턴 검출 기법을 소개 한다. 이는 로봇 시각의 응용문제에서 실내영상의 실시간 인식문제를 대상으로 한다. 검출 과정의 효율성 향상을 위하여 도입된 다중 필터는 후보 영역의 개수와 범위를 줄일 수 있게 한다. 제안된 모델에서 CNN 신경망은 가보변환(Gabor Transform)계층을 두어 검출 과정의 첫 단계에서 영상 내의 기본 특징 지도를 생성 하도록 하였다. 보다 강인한 검출기능을 위하여 조명보정 기법이 시스템의 전처리 단계로 구현 된다. 실제 영상을 통한 실험 결과로부터 제안된 이론의 타당성을 고찰 한다.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.151-156
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• 2015

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.