Palmprint recognition using Gabor feature-based (2D)2PCA

Pan, Xin; Ruan, Qiuqi

doi:10.1016/j.neucom.2007.12.030

Cited by 65 publications

(29 citation statements)

References 13 publications

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“…Gabor filters exhibit desirable characteristics of Spatial frequency(scaling),localization and Orientation selectivity and Gabor filter representation of face images(Gabor faces) are robust for illumination and expressional variability, so are used for face recognition. The dimensionality of Gabor feature space are intensively high, because Gabor faces are obtained by convolution of the face with the dozens of Gabor wavelets(filters).So ,compressed methods are employed to reduce the space dimension to avoid dealing with enormous data [77,78,79]. PCA, 2DPCA, LDA are most popular used dimension reduction algorithms [77,78,79].…”

Section: Overview Of Gabor Waveletmentioning

confidence: 99%

“…The dimensionality of Gabor feature space are intensively high, because Gabor faces are obtained by convolution of the face with the dozens of Gabor wavelets(filters).So ,compressed methods are employed to reduce the space dimension to avoid dealing with enormous data [77,78,79]. PCA, 2DPCA, LDA are most popular used dimension reduction algorithms [77,78,79]. And, we applied PCA algorithm toper form dimension reduction for feature extraction.…”

Section: Overview Of Gabor Waveletmentioning

confidence: 99%

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Face Recognition and Detection through Similarity Measurements

Bashir¹,

Koul²

2017

IJCA

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The facial recognition has been a problem worked on around the world for many persons, the problem has emerged in multiple fields and sciences, especially in computer science and other fields that are very interested in this technology are robotic, criminalist etc. Unfortunately, many reported face recognition techniques relay on the size and representative of training set such as e-passport, law enforcement and id-card identification, and most of them will suffer serious performance drop if only one training sample per person is available to the systems [1].In a face image, only a part of face is changed due to pose, illumination and other source of changes .In this paper, a novel face recognition detection approach known as Gabor wavelet based PCA approach is presented based on fusing global and local features of image. To extract global and local features, Gabor wavelet filter are applied on the whole image and non-overlapping sub-images with equal size. To reduce the dimension of new fused feature vector and to better characterize the similarity between each gallery face and the probe image set, Principal Component Analysis (PCA) is employed. And finally, measure the similarity between the images by using the Euclidean distance as classifier. The Experimental results shows that proposed technique improves the efficiency of face recognition under varying illumination, expression and variation in poses of face images by using standard databases when compared to traditional PCA and Conventional method such as global Gabor faces recognition. In this paper, the proposed algorithm is tested on the public and largely used ORL database.

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Section: Overview Of Gabor Waveletmentioning

confidence: 99%

Section: Overview Of Gabor Waveletmentioning

confidence: 99%

Face Recognition and Detection through Similarity Measurements

Bashir¹,

Koul²

2017

IJCA

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“…Similar work can be found in Ref. [48], which conducted two-step 2DPCA to reduce the dimension of Gabor feature space. The disadvantage of down-sampling is that a great number of discriminative Gabor features are discarded.…”

Section: Introductionmentioning

confidence: 76%

“…In the second experiment, we compare Log GMP based RCM method (i. e. our proposed method) with several recently proposed state-of-arts Gabor based methods, including G(2D) 2 PCA [48], GLRV [49], GRCM [53], EGRCM [54], PalmCode [12] and CompCode [37]. G(2D) 2 PCA, GLRV and GRCM methods only employ the GM information.…”

Section: Recognition Performance Comparisonmentioning

confidence: 99%

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Texture representation for low-resolution palmprint recognition

Mu¹

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Gray intensity image feature extraction of partial discharge in high-voltage cross-linked polyethylene power cable joint

Wang

Tang

Zhang

et al. 2012

Int. Trans. Electr. Energ. Syst.

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SUMMARY A feature extraction method for gray intensity image of partial discharge (PD) is applied to recognize the insulating defects in high‐voltage cross‐linked polyethylene power cable joint. The method is based on a two‐directional and two‐dimensional (2‐D) maximum margin criterion (MMC). A 2‐D orthogonal projection of gray intensity image of PD was performed in horizontal and vertical directions. Projected image data were taken as discriminant vector of different gray intensity images to solve the high dimensional and small sample size of PD gray intensity image. The nearest neighbor classifier was used to classify the PD gray intensity image to recognize different insulating defects in the joint. The recongnition results of four typical insulating defects in the laboratory indicated that the extraction speed of the gray intensity image feature and recognition rate of insulating defects are superior compared with the common principal component analysis and Fisher discriminant analysis with MMC. Copyright © 2012 John Wiley & Sons, Ltd.

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Palmprint recognition using Gabor feature-based (2D)2PCA

Cited by 65 publications

References 13 publications

Face Recognition and Detection through Similarity Measurements

Face Recognition and Detection through Similarity Measurements

Texture representation for low-resolution palmprint recognition

Gray intensity image feature extraction of partial discharge in high-voltage cross-linked polyethylene power cable joint

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