• 한국멀티미디어학회논문지
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• 제9권4호
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• pp.470-481
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• 2006

본 논문에서는 복합 컬러모델과 얼굴특정 정보를 이용하여 실시간으로 얼굴영역을 검출 추적하고 기울어진 얼굴영상을 보정하는 시스템을 제안하였다. 제안한 시스템은 YCbCr과 YIQ 컬러모텔을 사용하여 얼굴 후보영역을 검출하였다. 얼굴 후보영역에서 수평 수직 투영기법을 사용하여 얼굴을 검출하고 하우스도르프 정합 방법을 사용하여 얼굴을 추적하였다. 또한 검출된 얼굴영상으로부터 눈 특징자의 기울기 정보를 보정함으로써 얼굴 기울기를 보정하였다. 실험결과 제안한 알고리즘이 주위환경 변화가 있는 실시간 얼굴검출과 추적 및 기울어진 얼굴인식에 강인하였다. 실험에서는 110개의 테스트 얼굴 영상을 사용하여 좋은 성능결과를 얻었다. 실험결과 얼굴검출과 얼굴추적율은 각각 92.27%와 92.70%를 나타내었고 얼굴 정보들로부터 90.0%의 얼굴인식율을 얻었다.

• 전자공학회논문지CI
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• 제44권1호
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• pp.102-112
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• 2007

본 논문에서는 얼굴 인식 알고리즘을 하드웨어로 설계하여 임베디드 시스템에 적용하기 위해 고정 소수점 모델을 구성하고 그에 근거한 하드웨어 구조를 제안하였다. 얼굴 인식 알고리즘은 학습된 데이터를 사용하여 입력 영상에서 얼굴을 검출하고 검출된 얼굴 영상에서 두 눈을 찾아 얼굴 검증 단계를 거치며, 얼굴 검증단계에서 얻어진 두 눈의 위치를 이용하여 얼굴 인식 단계에서 필요한 얼굴 특징 벡터를 연산하고 저장 또는 비교를 통하여 얼굴 인식을 수행한다. 부동 소수점 모델과 고정 소수점 모델의 유사성은 부동 소수점 모델에서 검출된 영상을 고정 소수점 모델에서 수행하여 비교하였으며 성능이 0.2% 오차 범위 안에서 일치하는 고정 소수점 모델을 구성하였다.

• 스마트미디어저널
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• 제12권2호
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• pp.9-14
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• 2023

이 논문에서는 gradCAM를 활용한 적은 데이터로 얼굴 전체 또는 더 다양한 feature을 사용하여 얼굴인식을 할 수 있는 새로운 앙상블 방법론을 제안하였다. 인공지능 모델의 판단 근거는 gradCAM을 통하여 saliency map으로 표현될 수 있다. 따라서 본 논문에서는 학습된 얼굴인식 모델이 어느 부분에 편향적으로 관찰하여 판단했는지 gradCAM으로 시각화한다. 계산된 saliency map에서 일정 수치 이상의 돌출된 부분을 추가 모델이 학습에 사용할 수 없도록 노이즈를 추가해 데이터를 생산한다. 노이즈를 추가해서 만든 데이터로 학습할 경우 노이즈 부분을 활용하여 학습을 할 수 없으므로 새로운 얼굴 부분을 사용하여 얼굴인식 네트워크를 학습하게 된다. 기본 데이터로 학습한 네트워크와 돌출 부분에 노이즈를 추가해서 학습한 모델은 얼굴의 서로 다른 얼굴 feature을 사용할 수밖에 없고, 앙상블로 결합했을 때 얼굴의 좀 더 다양한 부분들을 사용한 임베딩 feature를 만들 수 있다. 이 논문에서 제안하는 앙상블 기법은 일반적인 앙상블 모델보다 정확도는 1.79% 상승하였고 equal error rate (EER)은 0.01788 감소하였다.

• 통합자연과학논문집
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• 제1권2호
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• pp.133-139
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• 2008

Face recognition technologies using PCA(principal component analysis) recognize faces by deciding representative features of faces in the model image, extracting feature vectors from faces in a image and measuring the distance between them and face representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of face models of the same class is used as recognition unit for the images inputted on a continual input image. This paper proposes a new PCA recognition in which database of faces.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2004-2007
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• 2003

This paper describes a method of performance enhancement using Flatness Mesure(FM) for the Gaussian Mixture Model(GMM) face recognition systems. Using this measure we discard the frames having low information before training and test. As the result, the performance increases about 9％ in the lower mixtures and calculation burden is decreased. As well, the recognition error rate is decreased under the illumination change surroundings. We use the 2D DCT coefficients lot face feature vectors and experiments are carried out on the Olivetti Research Laboratory (ORL) face database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.1100-1118
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• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

• 한국정보전자통신기술학회논문지
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• 제9권6호
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• pp.546-552
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• 2016

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1582-1590
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• 2022

Due to the situation of the widespread of the coronavirus, which causes the problem of lack of face image data occluded by masks at recent time, in order to solve the related problems, this paper proposes a method to generate face images with masks using a combination of generative adversarial networks and spatial transformation networks based on CNN model. The system we proposed in this paper is based on the GAN, combined with multi-scale convolution kernels to extract features at different details of the human face images, and used Wasserstein divergence as the measure of the distance between real samples and synthetic samples in order to optimize Generator performance. Experiments show that the proposed method can effectively put masks on face images with high efficiency and fast reaction time and the synthesized human face images are pretty natural and real.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.