• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.31-40
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• 2003

Most face recognition systems are based on 2D images and applied in many applications. However, it is difficult to recognize a face when the pose varies severely. Therefore, head pose estimation is an inevitable procedure to improve recognition rate when a face is not frontal. In this paper, we propose a novel head pose estimation algorithm for 3D face recognition. Given the 3D range image of an unknown face as an input, we automatically extract facial feature points based on the face curvature. We propose an Error Compensated Singular Value Decomposition (EC-SVD) method based on the extracted facial feature points. We obtain the initial rotation angle based on the SVD method, and perform a refinement procedure to compensate for remained errors. The proposed algorithm is performed by exploiting the extracted facial features in the normaized 3D face space. In addition, we propose a 3D nearest neighbor classifier in order to select face candidates for 3D face recognition. From simulation results, we proved the efficiency and validity of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.35-43
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• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.63-68
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• 2018

Interests in face recognition area have been increasing due to diverse emerging applications. Face recognition algorithm from a two-dimensional source could be challenging in dealing with some circumstances such as face orientation, illuminance degree, face details such as with/without glasses and various expressions, like, smiling or crying. Hopfield Network capabilities have been used specially within the areas of recalling patterns, generalizations, familiarity recognitions and error corrections. Based on those abilities, a specific experimentation is conducted in this paper to apply the Redundant Parallel Hopfield Network on a face recognition problem. This new design has been experimentally confirmed and tested to be robust in any kind of practical situations.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.135-138
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• 2001

본 논문에서는 3D 레이저 스캐너로 입력된 3차원 얼굴 영상에서 코의 특징 정보를 이용하여 얼굴을 인식하는 알고리즘을 제안하였다. 특히 3차원 영상은 주변의 조명 변화에 크게 영향을 받지 않는 장점이 있다. 이러한 정보를 이용하여, 제안된 알고리즘에서는 얼굴에서 가장 두드러지게 보이는 코의 3차원 정보를 이용하여 인식하는 알고리즘을 제안한다. 먼저 코를 추출한 다음, 회전된 3차원 영상에 대하여 정규화를 실시하고, 등고선을 이용한 영역기반의 방법과 특징기반의 방법을 이온하여 인식을 수행한다. 등고선을 이용한 영역기반은 3차원 얼굴 영상을 코끝의 좌표를 기준 점으로 등고선의 값이 10, 20, 30이 되는 영역을 추출 한 후 데이터 베이스 값들과 비교하여 각각의 차 영역에 대한 무게중심(X, Y), 픽셀 수, 분산을 구하여 순위가 가장 높은 것을 취한다. 특징 기반의 방법으로, 얼굴에 있어서의 실제의 코의 길이, 높이, 너비를 구하여 그 차가 가장 적은 것을 취한다. 위의 2가지 방법을 이용하여 인식을 수행 결과 100%의 인식률을 나타내었다.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.474-483
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• 2004

In this paper, we present a new Practical implementation of a person verification system using the profile of 3-dimensional(3D) face images based on weighted Hausdorff distance(WHD) used depth information. The approach works on finding the nose tip have protrusion shape on the face using iterative selection method to use a fiducial feint and extract the profile image from vertical 3D data for the nose tip. Hausdorff distance(HD) is one of usually used measures for object matching. This works analyze the conventional HD and WHD, which the weighted factor is depth information. The Ll measure for comparing two feature vectors were used, because it is simple and robust. In the experimental results, the WHD method achieves recognition rate of 94.3% when the ranked threshold is 5.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.748-755
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• 2010

In this paper we propose an improved face recognition method using Gabor tensor. Gabor transform is known to be able to represent characteristic feature in face and reduced environmental influence. It may contribute to improve face recognition ratio. We attempted to combine three-dimensional tensor from Gabor transform with MPCA(Multilinear PCA) and LDA. MPCA with tensor which use various features is more effective than traditional one or two dimensional PCA. It is known to be robust to the change of face expression or light. Proposed method is simulated by MATALB9 using ORL and Yale face database. Test result shows that recognition ratio is improved maximum 9~27% compared with exisisting face recognition method.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.21-28
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• 2011

A 3D face shape derived from 2D images may be useful in many applications, such as face recognition, face synthesis and human computer interaction. To do this, we develop a fast 3D Active Appearance Model (3D-AAM) method using depth estimation. The training images include specific 3D face poses which are extremely different from one another. The landmark's depth information of landmarks is estimated from the training image sequence by using the approximated Jacobian matrix. It is added at the test phase to deal with the 3D pose variations of the input face. Our experimental results show that the proposed method can efficiently fit the face shape, including the variations of facial expressions and 3D pose variations, better than the typical AAM, and can estimate accurate 3D face shape from images.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.348-351
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• 2004

In this paper, we propose the face recognition method using Harr wavelet transform and 2D PCA. While previous PCA computed the covariance matrix by using one dimensional vectors, 2D PCA computed the covarinace matrix by using direct two dimensional image and extracted feature vector by solving eigenvalue problem. To gain the face image having the low dimension and robust property, the proposed method uses wavelet transformation. We apply the LL band image data to 2D PCA for face recognition. The experimental results indicate that our method improves recognition rate than 2D PCA into original image.