• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.75-80
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• 2021

Artificial intelligence is becoming an important part of our lives providing incredible benefits. In this respect, facial expression recognition has been one of the hot topics among computer vision researchers in recent decades. Classifying small dataset of low resolution images requires the development of a new small scale deep CNN model. To do this, we propose a method suitable for small datasets. Compared to the traditional deep CNN models, this model uses only a fraction of the memory in terms of total learnable weights, but it shows very similar results for the FER2013 and FERPlus datasets.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3675-3682
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• 1999

This paper proposes the method to extract face components without using the color information and the motion information in a gray image. A laplacian pyramid of the original image is built. Eye and nose candidates are extracted using only the gray information in a low resolution laplacian image and pairs are found that consist of two eye candidates and a nose one. At full resolution, horizontal and vortical edges are found in the regions of face components which are established using the candidates. Using those edge informations, face components are extracted. The experiments have been performed for images with various sizes and positions of face, and show very encouraging result.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.85-87
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• 2022

최근 CCTV 출입 기록, 휴대폰 보안, 스마트 매장 등에서 얼굴 인식을 통해 개인을 식별하는 기술이 널리 사용되고 있다. 카메라의 각도, 조명, 사람의 움직임 등 얼굴 인식에 많은 외부 환경이 영향을 미치고 있지만 그중에서도 실제 영상에서 얼굴이 차지하는 영역이 작아 저해상도 얼굴 인식에 어려움을 겪고 있다. 이러한 문제점을 해결하고자 본 논문에서는 이미지 해상도가 얼굴 인식에 끼치는 영향을 알아보고 이미지 초해상화를 통해 얼굴 인식 성능을 개선하고자 한다. 쌍선형, 양3차 회선 보간법과 딥러닝 기반의 이미지 초해상화 모델인 RCAN을 이용하여 업스케일링한 데이터셋에 대해 학습한 ArcFace를 통해 얼굴 검증 평가를 진행하였다. 고해상도 이미지는 얼굴 인식 성능을 향상시키며, RCAN을 사용한 이미지 초해상화가 보간법을 사용한 방법보다 더 좋은 성능을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.597-600
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• 2007

Face is considered to be one of the biometrics in person identification. But Face recognition is a high dimensional pattern recognition problem. Even low-resolution face images generate huge dimensional feature space. The aim of this paper is to present a fast feature extraction method for real time human face recognition. first, It compute eigen-vector and eigen-value by Principle component analysis on inputed human face image, and propose method of feature extraction that make feature vector by apply gabor filter to computed eigen-vector. And it compute feature value which multiply by made eigen-value. This study simulations performed using the ORL Database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.44-51
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• 2009

This paper addresses a face verification method using the nonlinear composite filter. This face verification process can be simple and speedy because it does not require any reprocessing such as face detection, alignment or cropping. The optimum nonlinear composite filter is derived by minimizing the output energy due to additive noise and an input scene while maintaining the outputs of training images constant. The filter is equipped with the discrimination capability and the robustness to additive noise by minimizing the outputs of the input scene and the noise, respectively. We build the nonlinear composite filter with two training images and compare the filter with the conventional synthetic discriminant function (SDF) filter. The receiver operating characteristics (ROC) curves are presented as a metric for the performance evaluation. According to the experimental results the optimum nonlinear composite filter is shown to be a robust scheme for face verification in low resolution and noise environments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.112-113
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• 2013

서브 홀로그램 디스플레이는 디지털 홀로그래피 디스플레이의 제한된 시역을 관찰자의 동공 크기로 맞게 구현하여 사용자가 더 넓은 범위에서 더 큰 영상을 느끼도록 만드는 홀로그래픽 디스플레이이다. 본 논문에서는 서브홀로그램 방식에서 시야 창 문제를 해결하기 위해, Depth 카메라와 스테레오 카메라의 하이브리드 구성을 이용하여 정밀한 사용자 동공 추적 기법에 대해서 제안한다. 저해상도의 깊이 카메라의 얼굴 인식 정보를 바탕으로 고해상도 스테레오 카메라에서의 얼굴 및 눈의 후보영역을 찾고, 고해상도 스테레오 카메라에서 후보 영역 내의 동공 위치를 잦아서 빠르면서도 정밀한 동공 추적이 가능하도록 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.75-77
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• 2012

In this paper, We proposed that real-time pupil detection using local binarization at each region of eyes in image. In image obtained a single low-resolution web-camera, we detect a region of face using haar-like feature and then detect each region of eyes depending upon the rate of width and height of region of face respectively. In each region of eyes, we detect the pupil after local preprocessing and binarizing. This pupil detection can be variously used for HCI(Human-Computer Interface) systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.17-18
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• 2016

본 논문에서는 딥러닝 기술 중의 하나인 CNN(Convolutional Neural Network) 기반의 얼굴 표정 인식 기법을 제안한다. 제안한 기법에서는 획득한 여섯 가지 주요 표정의 얼굴영상들을 학습 데이터로 이용할 때 분류 성능을 저해시키는 과적합(over-fitting) 문제를 해결하기 위해서 데이터 증대 기법(data augmentation)을 적용한다. 또한 기존의 CNN 구조에서 convolutional layer 및 node의 수를 변경하여 학습 파라미터 수를 대폭 감소시킬 수 있다. 실험 결과 제안하는 데이터 증대 기법 및 개선한 구조가 높은 얼굴 표정 분류 성능을 보여준다는 것을 확인하였다.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1039-1046
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• 2015

We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.