• ETRI Journal
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• 제27권6호
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• pp.806-809
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• 2005

The main goal of this research is automatic video analysis using a face recognition technique. In this paper, an automatic video management system is introduced with a variety of functions enabled, such as index, edit, summarize, and retrieve multimedia data. The automatic management tool utilizes MPEG-7 visual descriptors to generate a video index for creating a summary. The resulting index generates a preview of a movie, and allows non-linear access with thumbnails. In addition, the index supports the searching of shots similar to a desired one within saved video sequences. Moreover, a face recognition technique is utilized to personalbased video summarization and indexing in stored video data.

• 반도체디스플레이기술학회지
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• 제7권3호
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• pp.45-49
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• 2008

Face detection and tracking technology on video sequence has developed indebted to commercialization of teleconference, telecommunication, front stage of surveillance system using face recognition, and video-phone applications. Complex background, color distortion by luminance effect and condition of luminance has hindered face recognition system. In this paper, we have proceeded to research of face recognition on video sequence. We extracted facial area using luminance and chrominance component on $YC_bC_r$ color space. After extracting facial area, we have developed the face recognition system applied to our improved algorithm that combined PCA and LDA. Our proposed algorithm has shown 92% recognition rate which is more accurate performance than previous methods that are applied to PCA, or combined PCA and LDA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권12호
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• pp.1247-1259
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• 2005

본 연구는 웹카메라와 같은 저해상도의 동영상으로부터 실시간 다중 얼굴 인식 시스템을 제안한다. 동영상을 이용한 얼굴 인식 시스템은 크게 얼굴 검출 단계와 얼굴 분류 단계로 나눌 수 있다. 첫째, 얼굴 검출 단계에서는 빠르고 강인한 객체 검출 성능을 가진 AdaBoost를 이용하여 얼굴 후보 영역을 검출하였고, 검출된 얼굴 후보 영역에 대한 주성분을 수행하여 데이타의 크기기 현저히 줄어든 특징 벡터를 구한 다음에 특징 벡터에 대해 SVM 기반 이진 분류를 수행하여 얼굴 후보 영역을 검증하였다. 둘째, 얼굴 분류 단계에는 주성분 분석과 멀티 SVM을 이용하여 각 얼굴들을 분류하였다. 실험 결과 본 논문에서 제안한 방법은 저해상도에서도 높은 얼굴 검출율과 동영상에서 실시간 처리가 가능한 빠른 다중 얼굴 검출과 인식 성능을 보였다. 또한 팬-틸트 기능을 가진 웹카메라를 이용한 자동 추적형 얼굴 인식 시스템을 적용하여 얼굴 검출 성능을 향상시켰고, 얼굴 인식 시스템의 응용으로 무선 On/off 얼굴인식 도어락 시스템을 구현하였다.

• 인지과학
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• 제11권2호
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• pp.79-87
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• 2000

본 논문에서는 MPEG 압축 비디오 상에서 얼굴 영역을 추출하고 이를 인식하는 방법에 대하여 제안한다. 제안된 방법은 크게 MPEG 압축 비디오의 처리를 위한 축소된 DC 영상의 구성단계, 축소된 DC 영상에서의 얼굴 영역 추출 단계, 그리고 얼굴 영역이 추출된 프레임에 대한 압축 복원 및 얼굴 인식의 3단계로 구성되어 있다. DC 영상의 구성 단계에서는 압축 복원 없이 DCT 계수의 DC 값과 2개의 AC 값만을 사용하여 부분적인 2차원 역 DCT 변환을 이용한 방법을 사용하였으며, 얼굴 영역 추출 단계에서는 DC 영상에 대해 얼굴의 색상 및 형태 정보를 이용한 얼굴 후보 영역 추출 방법과 K-L 변환 및 역 변환의 오차에 의한 얼굴 영역 추출 방법을 사용하였다. 얼굴 인식 단계에서는 얼굴 영역이 추출된 프레임에 대하여 GOP 단위의 압축 복원을 수행한 후 고유 얼굴 영상을 이용한 방법으로 얼굴 인식을 수행하였다. 제안된 방법의 성능을 검증하기 위하여 뉴스와 드라마 MPEG 비디오를 대상으로 실험을 수행하였으며, 실험 결과 제안된 방법이 효율적임을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1380-1390
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• 2021

The video face recognition (FR) is one of the most popular researches in the field of computer vision due to a variety of applications. In particular, research using the attention mechanism is being actively conducted. In video face recognition, attention represents where to focus on by using the input value of the whole or a specific region, or which frame to focus on when there are many frames. In this paper, we propose a novel attention based deep learning method. Main novelties of our method are (1) the use of combining two loss functions, namely weighted Softmax loss function and a Triplet loss function and (2) the feasibility of end-to-end learning which includes the feature embedding network and attention weight computation. The feature embedding network has a positive effect on the attention weight computation by using combined loss function and end-to-end learning. To demonstrate the effectiveness of our proposed method, extensive and comparative experiments have been carried out to evaluate our method on IJB-A dataset with their standard evaluation protocols. Our proposed method represented better or comparable recognition rate compared to other state-of-the-art video FR methods.

• 대한치과기공학회지
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• 제42권4호
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• pp.402-408
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• 2020

Purpose: To understand the students' level of recognition of online classes in the Department of Dental Technology and to provide the basic data for designing online classes based on the dental technology course. Methods: A survey was conducted among the students of the dental technology department. The collected data was analyzed with the SPSS ver. 25.0 program. To ensure a reliable verification, the α=0.05 significance level was used. The t-test and analysis of variance were also performed. Results: The students' level of recognition of online classes in the Department of Dental Technology is shown in the rate of recognition for video-based classes for both the theory and experiments. Students displayed high positivity with the video-based learning as it is repeated learning that is not affected by the limitations of time. In addition, video-based learning is highly beneficial in terms of convenience, satisfaction, and achievement for learning. Conclusion: Based on the results, video-based learning is a highly positive learning type for students. It was also recommended that the Department of Dental Technology should offer a post-COVID-19 online class to include the blended methods of a face-to-face class and video-based learning.

• 한국항행학회논문지
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• 제12권6호
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• pp.691-698
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• 2008

본 논문에서는 비디오 검색을 위한 새로운 얼굴 검출 및 인식 방법을 제안한다. 인물 정함은 비디오 프레임에서 어떻게 얼굴을 정확하게 찾아내는가에 달려 있다. 얼굴 영역은 Adaboost 알고리즘으로 부스트된 viola-jones의 특징을 이용하여 비디오 프레임에서 검출한다. 얼굴 검출 후 조명 보정을 하고 PCA(Principal Component Analysis)로 특징점을 추출하고 SVM(Support Vector Machine)으로 사람의 신원을 분류한다. 실험 결과 제안한 방법이 정합율면에서 우수한 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• 정보과학회 논문지
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• 제42권4호
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• pp.483-486
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• 2015

얼굴 인식기반의 사용자 보안 시스템은 접근이 허가된 사용자의 사진이나 비디오를 이용한 공격에 취약하다는 단점을 가지고 있다. 본 연구에서는 인증되지 않은 사용자가 비디오를 이용하여 시스템에 접근할 경우 해당 공격 시도를 검출하기 위한 위변조(Spoof) 검출 방법을 제안한다. 제안하는 방법은 연속된 3개의 Frame에서 푸리에 변환과 Dense-SIFT 구분자를 사용하여 400개의 실제 및 위변조 비디오 영상을 대상으로 실험한 결과 99%의 검출 정확도를 보였다.