• 한국정보과학회논문지:소프트웨어및응용
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• 제36권10호
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• pp.816-824
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• 2009

얼굴영상 공간에서 얼굴영상들은 조명이나 포즈에 의해 비선형적 분포를 갖는다. 이들을 선형모델에 기반을 둔 AAM으로 모델링 하는 것은 한계가 있다. 본 논문에서는 얼굴영상에 대한 몇 개의 군집이 주어졌다고 가정하고, 각 군집 별로 지역적인 AAM 모델을 구축하여 정합과정 중에 적합한 모델이 선택되도록 한다. 정합과정에서 발생하는 모델변경에 따른 모델간의 정합 인자 갱신의 문제는 인자 공간에서 모델간의 선형 관계를 미리 학습하여 해결한다. 심각한 정합 실패에 따른 잘못된 모델 선택을 줄이기 위해 점진적으로 모델변경이 이루어지도록 한다. 실험에서는 제안하는 방법을 Yale-B 조명얼굴 영상에 적용하여 모델을 생성하고 기존 방법과 정합 성능을 비교한다. 제안 방법은 심각한 그림자가 발생하는 강도 높은 조명얼굴 영상에서 성공적인 정합 결과를 보여주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2547-2567
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• 2021

It has been widely acknowledged that occlusion impairments adversely distress many face recognition algorithms' performance. Therefore, it is crucial to solving the problem of face image occlusion in face recognition. To solve the image occlusion problem in face recognition, this paper aims to automatically de-occlude the human face majority or discriminative regions to improve face recognition performance. To achieve this, we decompose the generative process into two key stages and employ a separate generative adversarial network (GAN)-based network in both stages. The first stage generates an initial coarse face image without an occlusion mask. The second stage refines the result from the first stage by forcing it closer to real face images or ground truth. To increase the performance and minimize the artifacts in the generated result, a new refine loss (e.g., reconstruction loss, perceptual loss, and adversarial loss) is used to determine all differences between the generated de-occluded face image and ground truth. Furthermore, we build occluded face images and corresponding occlusion-free face images dataset. We trained our model on this new dataset and later tested it on real-world face images. The experiment results (qualitative and quantitative) and the comparative study confirm the robustness and effectiveness of the proposed work in removing challenging occlusion masks with various structures, sizes, shapes, types, and positions.

• 한국정보전자통신기술학회논문지
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• 제7권1호
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• pp.55-60
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• 2014

본 논문에서는 자연 영상에서 피부색 색상과 채도를 기초로 얼굴영역을 추출하고 얼굴의 특징요소를 추출하는 방법을 제안하였다. 제안한 알고리즘은 조명보정과 얼굴 검출 과정으로 구성되었다. 조명 보정 과정에서는 조명변화에 대한 보정기능을 수행한다. 얼굴 검출 과정은 20개의 피부색 표본 영상에서 색상과 채도를 특징벡터로 사용, 입력영상과의 유클리디안 거리를 구하여 피부색 영역을 추출하였다. 추출된 얼굴 후보영역을 CMY칼라 모델에서 C요소로 눈을 검출하였고, YIQ 칼라 공간에서 Q요소로 입을 검출하였다. 추출된 얼굴 후보영역에서 일반적인 얼굴에 대한 지식을 기반으로 얼굴 영역을 검출하였다. 입력받은 10장의 자연 영상으로 실험한 결과 100%의 얼굴 검출율을 보였다.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• 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.

• 한국멀티미디어학회논문지
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• 제16권10호
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• pp.1156-1162
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• 2013

Face verification has been widely studied during the past two decades. One of the challenges is the rising concern about the security and privacy of the template database. In this paper, we propose a secure face verification system which generates a unique secure cryptographic key from a face template. The face images are processed to produce face templates or codes to be utilized for the encryption and decryption tasks. The result identity data is encrypted using Advanced Encryption Standard (AES). Distance metric naming hamming distance and Euclidean distance are used for template matching identification process, where template matching is a process used in pattern recognition. The proposed system is tested on the ORL, YALEs, and PKNU face databases, which contain 360, 135, and 54 training images respectively. We employ Principle Component Analysis (PCA) to determine the most discriminating features among face images. The experimental results showed that the proposed distance measure was one the promising best measures with respect to different characteristics of the biometric systems. Using the proposed method we needed to extract fewer images in order to achieve 100% cumulative recognition than using any other tested distance measure.

• 제어로봇시스템학회논문지
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• 제15권1호
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• pp.94-98
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• 2009

In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.

• 한국멀티미디어학회논문지
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• 제6권4호
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• pp.594-600
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• 2003

In this paper, we propose a detection method for facial features in color images with various backgrounds and face poses. To begin with, the proposed method extracts face candidacy region from images with various backgrounds, which have skin-tone color and complex objects, via the color and edge information of face. And then, by using the elliptical shape property of face, we correct a rotation, scale, and tilt of face region caused by various poses of head. Finally, we verify the face using features of face and detect facial features. In our experimental results, it is shown that accuracy of detection is high and the proposed method can be used in pose-invariant face recognition system effectively

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.412-420
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• 2020

This paper proposes a performance-improving face recognition system based on a super resolution method for low-resolution images. The conventional face recognition algorithm has a rapidly decreased accuracy rate due to small image resolution by a distance. To solve the previously mentioned problem, this paper generates a super resolution images based o deep learning method. The proposed method improved feature information from low-resolution images using a super resolution method and also applied face recognition using a feature extraction and an classifier. In experiments, the proposed method improves the face recognition rate when compared to conventional methods.

• 전자공학회논문지C
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• 제35C권10호
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• pp.48-58
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• 1998

신호처리 분야에서 미지의 신호원 분리에 주로 응용되는 독립 성분 분석법을 이용하여 얼굴인식을 할 수 있는 한 방식을 제안하였다. 하나의 얼굴영상 자체가 통계적으로 서로 독립인 어떤 미지의 특징영상의 합으로 표현될 수 있다고 가정하고 이 특징영상을 독립성분분석을 이용하여 구한 후, 새로운 얼굴이나 변화된 얼굴을 특징영상의 공간에 투영시켜 투영된 성분을 기준 얼굴영상과 비교하는 방법으로 인식을 수행하였다. 변화가 심한 여러 얼굴영상으로 구성된 데이터베이스(한 사람 당 10개씩의 변화된 400개의 얼굴 영상)에 대해 얼굴인식 실험을 수행하였고 또한 주성분 분석에 기초한 고유얼굴을 이용한 인식률과 비교 분석하였다. 제안된 방식은 주성분 분석법에 비해 높은 인식률을 제공하며, 특히 입력 얼굴 영상에 첨가되는 랜덤 잡음에 대단히 강한 특성을 갖는다.