Constrained Local Model과 Gabor Wavelets을 이용한 얼굴 인증 방법

박노진(Nojin Park),박충호(Chungho Park),노성혁(Sunghyeok No),곽노윤(Noyoon Kwak) 한국정보기술학회 2018 Proceedings of KIIT Conference Vol.2018 No.6

본 논문은 Constrained Local Model과 Gabor Wavelets을 이용한 얼굴 인증 방법에 관한 것이다. 제안된 방법은 CLM(Constrained Local Models) 기반의 얼굴 특징점 추출을 통해 얼굴의 주요 성분을 추출하는 과정과 이렇게 추출된 특징점을 대상으로 Gabor 웨이블릿 변환을 통해 Gabor 특징 벡터를 생성한 후, Gabor 특징 벡터들 간의 상호 상관도를 이용해 개인을 이증하는 과정으로 구성된다. Probe 얼굴 영상의 조명 상태가 정상 조도에서 저조도로 가변되는 상황에서도 평균 98.72%의 양호한 얼굴 인증률을 제공함을 확인할 수 있었다. 제안된 방법을 온라인 강의 모니터링 시스템, 온라인 심사, 의무 보수 교육 등과 같은 응용 분야에서 적용할 경우 양호한 성능을 제공할 것으로 기대된다. This paper relates to face verification method using constrained local model and Gabor wavelets. The proposed method extracts facial feature points based on Constrained Local Models (CLM), and extracts Gabor feature vectors using Gabor wavelets transform, and then verifies individuals using cross-correlation between Gabor feature vectors. The proposed method provides a good face recognition rate of 98.72% on the average even when the illumination condition of the probe face image changes from normal illumination to low illumination. The proposed method is expected to provide good performance when applied to applications such as online lecture monitoring system, online examination, and mandatory maintenance training.

Gabor 특징과 웨이브렛 영역의 BDIP와 BVLC 특징을 이용한 질감 특징 기반 언어 인식

장익훈(Ick Hoon Jang),이우신(Woo Shin Lee),김남철(Nam Chul Kim) 大韓電子工學會 2011 電子工學會論文誌-SP (Signal processing) Vol.48 No.4

본 논문에서는 Gabor 특징과 웨이브렛 영역의 BDIP와 BVLC 특징을 이용한 질감 특징 기반 언어 인식 방법을 제안한다. 제안된 방법에서는 먼저 시험 영상에 Gabor 변환과 웨이브렛 변환을 적용한다. 웨이브렛 영역의 상세 대역에는 Donoho의 연역치화를 적용하여 잡음을 제거한다. 이어서 Gabor 영상에는 크기 연산자를 적용하고 웨이브렛 부대역에는 BDIP와 BVLC 연산자를 적용한다. 그런 다음 Gabor 크기 영상과 BDIP, BVLC 부대역에 대하여 통계치를 계산하여 그 결과들을 벡터화하고 융합하여 특징 벡터로 사용한다. 분류 단계에서는 얼굴 인식에 주로 사용되는 WPCA를 분류기로 하여 시험 특징 벡터와 가장 유사한 학습 특징 벡터를 찾는다. 실험 결과 제안된 방법은 실험 문서 영상 DB에 대하여 비교적 낮은 특징 벡터 차원으로 매우 우수한 언어 인식 성능을 보여준다. In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho’s soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

Comparative Analysis of Wavelet-Based Scale-Invariant Feature Extraction Using Different Wavelet Bases

Joohyun Lim,Youngouk Kim,Joonki Paik 보안공학연구지원센터(IJSIP) 2009 International Journal of Signal Processing, Image Vol.2 No.4

The Robust Derivative Code for Object Recognition

( Hainan Wang ),( Baochang Zhang ),( Hong Zheng ),( Yao Cao ),( Zhenhua Guo ),( Chengshan Qian ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.1

This paper proposes new methods, named Derivative Code (DerivativeCode) and Derivative Code Pattern (DCP), for object recognition. The discriminative derivative code is used to capture the local relationship in the input image by concatenating binary results of the mathematical derivative value. Gabor based DerivativeCode is directly used to solve the palmprint recognition problem, which achieves a much better performance than the state-of-art results on the PolyU palmprint database. A new local pattern method, named Derivative Code Pattern (DCP), is further introduced to calculate the local pattern feature based on Dervativecode for object recognition. Similar to local binary pattern (LBP), DCP can be further combined with Gabor features and modeled by spatial histogram. To evaluate the performance of DCP and Gabor-DCP, we test them on the FERET and PolyU infrared face databases, and experimental results show that the proposed method achieves a better result than LBP and some state-of-the-arts.

Near-infrared face recognition by fusion of E-GV-LBP and FKNN

( Weisheng Li ),( Lidou Wang ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.1

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets

( Jun Huang ),( Xiuhui Wang ),( Jun Wang ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.4

The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.

Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets

Huang, Jun,Wang, Xiuhui,Wang, Jun Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.4

The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.

Fabric Defect Detection Using Adaptively Tuned Gabor Filters

Luo Jie,Hu Quan,Bi Mingde,Ao Fei 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

A new fabric defect detection algorithm base on Gabor filters is proposed. The spectral characteristics of both fabric texture and defects are analyzed. Gabor wavelet which can be considered as a bank of Gabor filters are used for the decomposition of fabric image. Based on spectral characteristics of fabric texture and defects, a new tuning method of Gabor wavelet is proposed to enhance the energy of defective region and attenuate the energy of normal texture. Decomposition images from different scales and orientations are fused into a single one to emphasize the presence of different kinds of defects. For comparison, the performance of proposed method as well as other two other defect detection methods using Gabor filters is evaluated with typical fabric defect samples. The experiment results obtained indicate that the proposed method is more effective than the other two.

Seafloor Classification Based on the Texture Analysis of Sonar Images Using the Gabor Wavelet

Sun, Ning,Shim, Tae-Bo The Acoustical Society of Korea 2008 韓國音響學會誌 Vol.27 No.e3

In the process of the sonar image textures produced, the orientation and scale factors are very significant. However, most of the related methods ignore the directional information and scale invariance or just pay attention to one of them. To overcome this problem, we apply Gabor wavelet to extract the features of sonar images, which combine the advantages of both the Gabor filter and traditional wavelet function. The mother wavelet is designed with constrained parameters and the optimal parameters will be selected at each orientation, with the help of bandwidth parameters based on the Fisher criterion. The Gabor wavelet can have the properties of both multi-scale and multi-orientation. Based on our experiment, this method is more appropriate than traditional wavelet or single Gabor filter as it provides the better discrimination of the textures and improves the recognition rate effectively. Meanwhile, comparing with other fusion methods, it can reduce the complexity and improve the calculation efficiency.

Facial Expression Recognition with Fuzzy C-Means Clustering Algorithm and Neural Network Based on Gabor Wavelets

( Young Suk Shin ),( Chan Sup Chung ),( Yill Byung Lee ) 한국감성과학회 2000 춘계학술대회 Vol.2000 No.-

This paper presents a facial expression recognition based on Gabor wavelets that uses a fuzzy Cmeans( FCM) clustering algorithm and neural network. Features of facial expressions are extracted to two steps. In the first step, Gabor wavelet representation can provide edges extraction of major face components using the average value of the image`s 2-D Gabor wavelet coefficient histogram. In the next step, we extract sparse features of facial expressions from the extracted edge information using FCM clustering algorithm. The result of facial expression recognition is compared with dimensional values of internal states derived from semantic ratings of words related to emotion. The dimensional model can recognize not only six facial expressions related to Ekman`s basic emotions, but also expressions of various internal states.