• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.131-146
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• 2021

Multimodal biometric-based recognition has been an active topic because of its higher convenience in recent years. Due to high user convenience of finger, finger-based personal identification has been widely used in practice. Hence, taking Finger-Print (FP), Finger-Vein (FV) and Finger-Knuckle-Print (FKP) as the ingredients of characteristic, their feature representation were helpful for improving the universality and reliability in identification. To usefully fuse the multimodal finger-features together, a new robust representation algorithm was proposed based on hierarchical model. Firstly, to obtain more robust features, the feature maps were obtained by Gabor magnitude feature coding and then described by Local Binary Pattern (LBP). Secondly, the LGBP-based feature maps were processed hierarchically in bottom-up mode by variable rectangle and circle granules, respectively. Finally, the intension of each granule was represented by Local-invariant Gray Features (LGFs) and called Hierarchical Local-Gabor-based Gray Invariant Features (HLGGIFs). Experiment results revealed that the proposed algorithm is capable of improving rotation variation of finger-pose, and achieving lower Equal Error Rate (EER) in our homemade database.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4903-4929
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• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• Journal of Korea Multimedia Society
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• v.6 no.4
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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

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.207-211
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• 2011

This paper presents a method for facial feature representation and recognition from the Covariance Matrices of the Gabor-filtered images. Gabor filters are a very powerful tool for processing images that respond to different local orientations and wave numbers around points of interest, especially on the local features on the face. This is a very unique attribute needed to extract special features around the facial components like eyebrows, eyes, mouth and nose. The Covariance matrices computed on Gabor filtered faces are adopted as the feature representation for face recognition. Geodesic distance measure is used as a matching measure and is preferred for its global consistency over other methods. Geodesic measure takes into consideration the position of the data points in addition to the geometric structure of given face images. The proposed method is invariant and robust under rotation, pose, or boundary distortion. Tests run on random images and also on publicly available JAFFE and FRAV3D face recognition databases provide impressively high percentage of recognition.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2096-2099
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• 2003

본 논문에서는 입력 영상의 컬러 정보를 이용함으로써 조명 변화나 얼굴의 자세 변화에 둔감하게 얼굴 정보를 고속 검출하는 알고리듬을 제안하였다 계산복잡도가 작으면서도, 조명의 변화에 민감하지 않은 특성을 가진 NCC (normalized color component) 좌표계에서 정의한 살색에 기반하여 얼굴 후보 영역을 검출하고, 검출된 얼굴 후보 영역 내에서의 눈의 검출에도 색상 분포 특성을 이용함으로써 얼굴의 숙임(nod), 돌림(shake), 기울임(tilt)등에 의한 자세 변화에 대해서도 둔감하게 두 눈의 위치를 고속으로 찾도록 하였다. 특히 집중자(concentrator)를 제안 적용하여 유동적인 눈썹의 영향을 줄이고 눈안의 중심 위치를 찾도록 가중치 눈지도(eye map)를 도입하였다. 제안된 알고리듬이 조명 변화나 얼굴의 다양한 자세 변화가 있는 영상에서 얼굴 후보 영역과 두 눈의 위치를 효과적으로 검출함을 실험을 통해 확인하였다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1909-1910
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• 2008

본 논문에서는 실린더 모델을 이용하여 머리의 다양한 포즈 변화와 조명 변화에 대해 강인한 얼굴 인식을 제안하고자 한다. 실린더 모델은 사람의 머리가 실린더 모양과 유사하고 그 표면은 얼굴에 해당된다고 가정한다. 실린더 모델은 6가지의 모션 파라메터를 따라 움직이며 Lucas-Kanade 알고리즘에 의해 모션 파라메터의 양을 결정한다. 강인한 동작을 위해 템플릿을 지속적으로 바꿔주는 동적 템플릿(dynamic template)방법과 그에 따른 에러가 누적되는 것을 막기 위해 re-registration방법을 사용한다. 조명 문제를 해결하기 위해 템플릿에서 조명 주성분 벡터를 추출하여 제거하는 방법으로 조명 효과를 제거한다. 실험에서는 다양한 포즈 변화와 조명 변화가 반영된 얼굴 데이터베이스를 구축하고 추출한 텍스쳐 맵(texture map image)을 SVM에 적용함으로서 포즈, 조명 변화에 강인한 얼굴인식을 보인다.