• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• 방송공학회논문지
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• 제9권2호
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• pp.164-173
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• 2004

본 논문에서는 등장인물 검출 및 인식과 함께 등장인물의 출연 구간 분석이 가능한 시스템을 제안한다. 드라마, 스포츠와 같은 방송 비디오는 그 특성상 인물이 중심이 되며 각 시점에 등장하는 주요 인물은 방송용 비디오의 중요한 특징이 된다. 본 논문에서는 비디오에서 등장하는 주요 인물을 자동으로 분석하는 ACAV(Automatic Cast-list Analysis in Videos) 시스템을 제안한다. ACAV 시스템은 등장인물을 자동 검출하여 인물 DB에 등록하는 FAGIS(FAce reGIStration)와 생성된 인물 DB을 이용하여 등장인물을 분석하는 FACOG(FAce reCOGnition)로 구성된다. 기존의 상용화된 등장인물 분석 시스템인 FaceIt과의 성능 비교를 통해 ACAV의 성능을 검증하였다. 얼굴 검출 실험에서 ACAV의 얼굴 검출률은 84.3%로 FaceIt 보다 약 30% 높았고, 얼굴 인식 실험에서도 ACAV의 얼굴 인식률은 75.7%로 FaceIt 보다 27.5% 높은 성능을 보였다. ACAV 시스템은 방송 멀티미디어 공급자를 위한 대용량 비디오 관리 시스템으로 이용될 수 있으며 일반 사용자를 대상으로 한 PVR(Personal Video Recorder), 모바일 폰 등의 비디오 관리 시스템으로도 이용될 수 있다.

• International journal of advanced smart convergence
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• 제11권3호
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• pp.64-71
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• 2022

Various types of attendance management systems are being introduced in a remote working environment and research on using face recognition is in progress. To ensure accurate worker's attendance, a face recognition-based attendance management system must analyze every frame of video, but face recognition is a heavy task, the number of the task should be minimized without affecting accuracy. In this paper, we proposed a search model using time interval variation to minimize the number of face recognition task of recorded videos for attendance management system. The proposed model performs face recognition by changing the interval of the frame identification time when there is no change in the attendance status for a certain period. When a change in the face recognition status occurs, it moves in the reverse direction and performs frame checks to more accurate attendance time checking. The implementation of proposed model performed at least 4.5 times faster than all frame identification and showed at least 97% accuracy.

• 한국컴퓨터정보학회논문지
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• 제16권10호
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• pp.53-62
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• 2011

본 논문에서는 첨단 IT융,복합기술의 발달로 특수 기술로만 여겨졌던 영상객체인식 기술분야가 스마트-폰 기술의 발전과 더불어 개인 휴대용 단말기기로 발전하고 있다. 3D기반의 얼굴인식 검출기술은 객체인식 기술을 통하여 지능형 영상검출 인식기술기술로 진화되고 있음에 따라 영상인식을 통한 얼굴검출기술과 더불어 개발속도가 급속히 발전하고 있다. 본 논문에서는 휴먼인식기술을 기반으로 한 얼굴객체인식 영상검출을 통한 얼굴인식처리 기술의 인지 적용기술을 IP카메라에 적용하여 인가자의 입,출입등의 식별능력을 적용한 휴먼인식을 적용한 얼굴측정 기술에 대한 연구방안을 제안한다. 연구방안은 1)얼굴모델 기반의 얼굴 추적기술을 개발 적용하였고 2)개발된 알고리즘을 통하여 PC기반의 휴먼인식 측정 연구를 통한 기본적인 파라미터 값을 CPU부하에도 얼굴 추적이 가능하며 3)양안의 거리 및 응시각도를 실시간으로 추적할 수 있는 효과를 입증하였다.

• 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.

• 한국응급구조학회지
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• 제24권3호
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• pp.55-63
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• 2020

Purpose: This study aimed to establish effective training strategies and methods by comparing the effects of 360° virtual reality video and first-person video in non-face-to-face practical lectures. Methods: This crossover study, implemented May 18-31, 2020, included 27 participants. We compared 360° virtual reality video and first-person video. SPSS version 25.0 was used for statistical analysis. Results: The 360° virtual reality video had a higher score of experience recognition (p=.039), vividness (p=.045), presence (p=.000), fantasy factor (p=.000) than the first-person video, but no significant difference was indicated for satisfaction (p=.348) or interest (p=.441). Conclusion: 360° virtual reality video and first-person video can be used as training alternatives to achieve the standard educational objectives in non-face-to-face practical lectures.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• 한국지능시스템학회논문지
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• 제23권5호
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• pp.423-430
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• 2013

통상의 얼굴인식은 사람이 똑바로 카메라를 응시해야 하거나, 혹은 이동하는 통로의 정면과 같이 특정 얼굴포즈를 취득할 수 있는 위치에 카메라를 설치하는 등 통제적인 환경에서 이루어진다. 이러한 제약은 사람에게 불편을 초래하고 얼굴인식의 적용 범위를 제한하는 문제가 있다. 본 논문은 이러한 기존방식의 한계를 극복하기 위하여 대상이 특별한 제약 없이 자유롭게 움직이더라도 동영상 내에서 대상의 얼굴을 추적하고 얼굴인식을 하는 방법을 제안한다. 먼저 동영상 속의 얼굴은 IVT(Incremental Visual Tracking) 추적기를 사용하여 지속적으로 추적이 되며 이때 얼굴의 크기변화와 기울기가 보상이 되어 추출이 된다. 추출된 얼굴영상은 사람과 카메라의 각도를 특정각도로 제한하지 않았으므로 다양한 포즈를 가지게 되며 따라서 얼굴인식을 하기 위해서 포즈에 대한 판정이 선행되어야 한다. 본 논문에서는 PCA(Principal Component Analysis)기반의 얼굴포즈판정방법을 사용하여 추적기에서 추출된 이미지가 5개 포즈별 DB속의 학습된 포즈와 유사한 것으로 판정될 때만 얼굴인식을 수행하여 인식률을 높이는 방법을 제안하였다. 얼굴인식에서는 PCA, 2DPCA, $(2D)^2PCA$의 인식알고리즘을 사용하여 얼굴인식률과 수행시간을 비교 제시하였다.

• 디지털산업정보학회논문지
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• 제11권3호
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• pp.99-112
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• 2015

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.