• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.608-611
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• 2004

본 논문에서는 고유얼굴 특성에 기반한 강건한 얼굴 인식 기술을 제안한다. 전형적인 고유얼굴 인식방법은 학습영역에서 고유얼굴을 생성시키고, 모든 학습영상을 이 얼굴공간에 투영시켜 각각의 사람마다 저장된 성분들을 비교하거나 상관시켜 특징들을 추출합니다. 복잡한 배경에 있는 얼굴들을 인식할 때 EFR방법은 얼굴인식에는 강하지만, 얼굴과 배경들 사이의 구분을 실패하게 된다 배경에서 강건한 얼굴인식을 위해서 배경패턴을 학습하며, 배경영역은 배경패턴으로부터 생성되어 얼굴영역과 함께 얼굴 인식을 위하여 사용된다. 본 논문에서 제안한 방법이 EFR방법보다 성능과 복잡한 배경하에서 매우 좋은 결과를 나타냄을 확인할 수 있었다.

• 로봇학회논문지
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• 제4권4호
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• pp.257-264
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• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.289-294
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• 2019

As social network service platforms grow and one-person media market expands, people upload their own photos and/or videos through multiple open platforms. However, it can be illegal to upload the digital contents containing the faces of others on the public sites without their permission. Therefore, many people are spending much time and effort in editing such digital contents so that the faces of others should not be exposed to the public. In this paper, we propose an automatic face hiding system called 'autoblur', which detects all the unregistered faces and mosaic them automatically. The system has been implemented using the GitHub MIT open-source 'Face Recognition' which is based on deep learning technology. In this system, two dozens of face images of the user are taken from different angles to register his/her own face. Once the face of the user is learned and registered, the system detects all the other faces for the given photo or video and then blurs them out. Our experiments show that it produces quick and correct results for the sample photos.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.1006-1009
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• 2003

In this paper, we propose a technique of the recognition of partial face. Most of the research is concentrated on the recognition of whole face Since part of the face area in an image can be damaged or overlapped, face recognition based on partial face is required. PCA and LDA technique is applied to the recognition of partial face. Also, a new method to combine the results of the recognition of parts of the face.

• 한국통신학회논문지
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• 제30권6C호
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• pp.570-576
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• 2005

얼굴 인식 분야에서 포즈의 변화는 인식률을 저하시키는 가장 심각한 문제로 알려져 있다. 본 논문에서는 이러한 포즈가 변화된 얼굴 영상에 대한 인식률을 높이기 위한 전처리 단계로 정면이 아닌 얼굴 영상을 정면 얼굴 영상으로 변환시키는 방법을 제안한다. 제안한 방법은 PCA 계수를 선형 변환 시키는 변환 행렬을 사용되는데 이 변환 행렬은 PCA 계수 사이의 선형적인 관계를 이용하여 구한다. 제안된 방법은 PCA/LDA를 이용한 얼굴 인식 알고리즘으로 검증하였으며, 실험 결과 제안된 방법이 얼굴 인식률을 $20\%$ 정도 향상시킴을 알 수 있었다.

• 지능정보연구
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• 제2권1호
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• pp.45-58
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• 1996

This paper describes an a, pp.oach to recognizing composite features of prismatic parts. AAG (Attribute Adjacency Graph) is adopted as the basis of describing basic feature, but it is extended to enhance the expressive power of AAG by adding face type, angles between faces and normal vectors. Our a, pp.oach is called Extended AAG (EAAG). To simplify the recognition procedure, feature classification tree is built using the graph types of EEA and the number of EAD's. Algorithms to find open faces and dimensions of features are exemplified and used in decomposing composite feature. The processing sequence of recognized features is automatically determined during the decomposition process of composite features.

• 전자공학회논문지
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• 제53권2호
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• pp.97-105
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• 2016

본 논문에서는 얼굴 영상의 지역 정보를 효과적으로 활용하기 위해, 부분 영상에 대한 다중 해상도 히스토그램을 이용한 얼굴 인식 방법을 제안한다. 기존 DFD의 경우 단일 크기로 나누어진 부분 영역의 히스토그램을 통합하여 유사도를 비교하나, 이는 부분 가림이나 조명변이로 인해 변형된 영역이 단일 부분 영역 내에서 발생하지 않고 여러 개의 부분 영역에 걸쳐 발생할 수 있기 때문에, 지역 정보들의 특성을 활용하는 데에 효과적이지 못하다. 본 논문에서는 각각의 부분 영역에 대해 다중 해상도로 분할하여 여러 종류의 크기에 해당하는 부영역의 히스토그램을 사용함으로써, 인식 과정에서 지역 정보의 손실을 최소화하고자 하였다. YaleB, AR, CAS-PEAL-R1 데이터베이스에 대해 인식 실험을 수행한 결과, 제안한 방법이 여러 종류의 변이가 있는 경우에 인식 성능을 향상시키는 것을 확인 할 수 있었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2263-2266
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• 2003

Three-dimensional face recognition algorithm using curvature information representing characteristics of surface form is suggested. The experiment showed more than 90 percent of recognition for the noses which had definite change value of data, and contained much information about surface curvature. Recognition ratio using a contour taken from the remaining part other than the eyes, noses, mouths which are the main components of faces showed the important role, which could be used as the important index information in the three-dimensional face recognition.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1145-1157
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• 2020

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.