A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric m...A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.展开更多
Wavelet decomposition has been applied in palmprint recognition successfully. However, only the low frequency sub-band was used for further feature extraction, while the high frequency sub-bands were consid2 ered to b...Wavelet decomposition has been applied in palmprint recognition successfully. However, only the low frequency sub-band was used for further feature extraction, while the high frequency sub-bands were consid2 ered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. In this pa- per, we firstly investigate the performances of all the sub-bands by using principal component analysis （PCA） on the BJTU and PolyU palmprint databases, and then use mean filtering to enhance the robustness of the high frequency sub-bands. We find that the preprocessed high frequency sub-bands not only can be used for palm- print recognition but also contain complementary information with the low frequency sub-band. The experimental results show that the performances of the horizontal and vertical high frequency sub-bands can be promoted up to a competitive level, and the fusion scheme, which combines the matching scores of high frequency sub-bands with that of low frequency sub-band, is superior to the conventional recognition methods.展开更多
A novel coding based method named as local binary orientation code （LBOCode） for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation inf...A novel coding based method named as local binary orientation code （LBOCode） for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation information is attained with a winner-take-all rule. Subsequently, the resulting orientation mapping array is operated by uniform local binary pattern. Accordingly, LBOCode image is achieved which contains palmprint orientation information in pixel level. Further we divide the LBOCode image into several equal-size and nonoverlapping regions, and extract the statistical code histogram from each region independently, which builds a global description of palmprint in regional level. In matching stage, the matching score between two palmprints is achieved by calculating the two spatial enhanced histograms＇ dissimilarity, which brings the benefit of computational simplicity. Experimental results demonstrate that the proposed method achieves more promising recognition performance compared with that of several state-of-the-art methods.展开更多
Biometric identification was a kind of identity recognition technology by making use of the human's unique physiological or behavioral characteristics,it provided a high reliability and stability way for the ident...Biometric identification was a kind of identity recognition technology by making use of the human's unique physiological or behavioral characteristics,it provided a high reliability and stability way for the identification. Global threshold binarization palmprint image is used in this paper,and the bio-morphological methods are used to get the sensitive area of palmprint image's positioning point,so as to extract the region of interest. The palmprint collection is realized on the FPGA chip,and this kind of collection method uses the DSP Builder toolbox to realize visual programming in Matlab / Simulink and achieve fast modeling and development. The practice proves that this method is simple,flexible and its equipment is portable and fast.展开更多
For a large-scale palmprint identification system,it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy.In this paper,we propose a n...For a large-scale palmprint identification system,it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy.In this paper,we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification.By investigating hashing-based algorithms,we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable.Secondly,we propose a window-based feature measurement for rapid searching of the target.We explore the influence of parameters related to hashing-based palmprint identification.We have carried out a number of experiments on the Hong Kong Poly U large-scale database and the CASIA palmprint database plus a synthetic database.The results show that on the Hong Kong Poly U large-scale database,the proposed method is about 1.5 times faster than the state-of-the-art ones,while achieves the comparable identification accuracy.On the CASIA database plus the synthetic database,the proposed method also achieves a better performance on identification speed.展开更多
In order to make the environment of palmprint recognition more flexible and improve the accuracy of touchless palmprint recognition. This paper proposes a robust, touchless, palmprint recognition system which is based...In order to make the environment of palmprint recognition more flexible and improve the accuracy of touchless palmprint recognition. This paper proposes a robust, touchless, palmprint recognition system which is based on color palmprint images. This system uses skin-color thresholding and hand valley detection algorithm for extracting palmprint. Then, the local binary pattern (LBP) is applied to the palmprint in order to extract the palmprint features. Finally, chi square statistic is used for classification. The experimental results present the equal error rate of 3.7668% and correct recognition rate of 97.0142%. Therefore the results show that this approach is robust and efficient in color palmprint images which are acquired in lighting changes and cluttered background for touch-less palmprint recognition system.展开更多
This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characte...This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, the fused image is convolved with Gabor wavelet transform. The Gabor wavelet based feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony optimization algorithm is applied to consider only relevant, distinctive and reduced feature set from Gabor responses. Finally, the reduced set of features is trained with support vector machines and accomplished user recognition tasks. For evaluation, CASIA multispectral palmprint database is used. The experimental results reveal that the system is robust and encouraging while variations of classifiers are used.展开更多
Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs...Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems.Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification.This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication.A HAMTE-Siamese network is constructed,which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users.The HAMTE is generated for each user during the enrollment phase,which is responsible for generating a secure template for the enrolled user.The proposed network secures the person’s Palmprint template by translating it into an irreversible template(different features space).It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person’s template from being stolen.Experimental results are conducted on the CASIA database,where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates.The recognition accuracy deviated by around 3%,and the equal error rate(EER)by approximately 0.02 compared to the original data,with appropriate performance(approximately 13 ms)while preserving the irreversibility property of the secure template.Moreover,the brute-force attack has been analyzed under the new Palmprint protection scheme.展开更多
Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational compl...Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.展开更多
In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use in...In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.展开更多
基金National Research Foundation of Korea funded by the Ministry of Education,Science and Technology,Grant/Award Number:NRF-2021R1A2C1093425。
文摘A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 60773015)Beijing Natural Science Foundation (Grant No. 4102051)the Fundamental Research Funds for the Central Universities (Grant No. 2009JBZ006)
文摘Wavelet decomposition has been applied in palmprint recognition successfully. However, only the low frequency sub-band was used for further feature extraction, while the high frequency sub-bands were consid2 ered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. In this pa- per, we firstly investigate the performances of all the sub-bands by using principal component analysis （PCA） on the BJTU and PolyU palmprint databases, and then use mean filtering to enhance the robustness of the high frequency sub-bands. We find that the preprocessed high frequency sub-bands not only can be used for palm- print recognition but also contain complementary information with the low frequency sub-band. The experimental results show that the performances of the horizontal and vertical high frequency sub-bands can be promoted up to a competitive level, and the fusion scheme, which combines the matching scores of high frequency sub-bands with that of low frequency sub-band, is superior to the conventional recognition methods.
基金supported partly by the National Grand Fundamental Research 973 Program of China under Grant No. 2004CB318005the Doctoral Candidate Outstanding Innovation Foundation under Grant No.141092522the Fundamental Research Funds under Grant No.2009YJS025
文摘A novel coding based method named as local binary orientation code （LBOCode） for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation information is attained with a winner-take-all rule. Subsequently, the resulting orientation mapping array is operated by uniform local binary pattern. Accordingly, LBOCode image is achieved which contains palmprint orientation information in pixel level. Further we divide the LBOCode image into several equal-size and nonoverlapping regions, and extract the statistical code histogram from each region independently, which builds a global description of palmprint in regional level. In matching stage, the matching score between two palmprints is achieved by calculating the two spatial enhanced histograms＇ dissimilarity, which brings the benefit of computational simplicity. Experimental results demonstrate that the proposed method achieves more promising recognition performance compared with that of several state-of-the-art methods.
文摘Biometric identification was a kind of identity recognition technology by making use of the human's unique physiological or behavioral characteristics,it provided a high reliability and stability way for the identification. Global threshold binarization palmprint image is used in this paper,and the bio-morphological methods are used to get the sensitive area of palmprint image's positioning point,so as to extract the region of interest. The palmprint collection is realized on the FPGA chip,and this kind of collection method uses the DSP Builder toolbox to realize visual programming in Matlab / Simulink and achieve fast modeling and development. The practice proves that this method is simple,flexible and its equipment is portable and fast.
基金supported in part by the National Natural Science Foundation of China(61806071)the Natural Science Foundation of Hebei Province(F2019202464,F2019202381)+2 种基金the Open Project Program of the National Laboratory of Pattern Recognition(NLPR)of China(201900043)Hebei Provincial Education Department Youth Foundation(QN2019207)the Technical Expert Project of Tianjin(19JCTPJC55800,19JCTPJC57000)。
文摘For a large-scale palmprint identification system,it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy.In this paper,we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification.By investigating hashing-based algorithms,we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable.Secondly,we propose a window-based feature measurement for rapid searching of the target.We explore the influence of parameters related to hashing-based palmprint identification.We have carried out a number of experiments on the Hong Kong Poly U large-scale database and the CASIA palmprint database plus a synthetic database.The results show that on the Hong Kong Poly U large-scale database,the proposed method is about 1.5 times faster than the state-of-the-art ones,while achieves the comparable identification accuracy.On the CASIA database plus the synthetic database,the proposed method also achieves a better performance on identification speed.
文摘In order to make the environment of palmprint recognition more flexible and improve the accuracy of touchless palmprint recognition. This paper proposes a robust, touchless, palmprint recognition system which is based on color palmprint images. This system uses skin-color thresholding and hand valley detection algorithm for extracting palmprint. Then, the local binary pattern (LBP) is applied to the palmprint in order to extract the palmprint features. Finally, chi square statistic is used for classification. The experimental results present the equal error rate of 3.7668% and correct recognition rate of 97.0142%. Therefore the results show that this approach is robust and efficient in color palmprint images which are acquired in lighting changes and cluttered background for touch-less palmprint recognition system.
文摘This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, the fused image is convolved with Gabor wavelet transform. The Gabor wavelet based feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony optimization algorithm is applied to consider only relevant, distinctive and reduced feature set from Gabor responses. Finally, the reduced set of features is trained with support vector machines and accomplished user recognition tasks. For evaluation, CASIA multispectral palmprint database is used. The experimental results reveal that the system is robust and encouraging while variations of classifiers are used.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems.Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification.This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication.A HAMTE-Siamese network is constructed,which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users.The HAMTE is generated for each user during the enrollment phase,which is responsible for generating a secure template for the enrolled user.The proposed network secures the person’s Palmprint template by translating it into an irreversible template(different features space).It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person’s template from being stolen.Experimental results are conducted on the CASIA database,where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates.The recognition accuracy deviated by around 3%,and the equal error rate(EER)by approximately 0.02 compared to the original data,with appropriate performance(approximately 13 ms)while preserving the irreversibility property of the secure template.Moreover,the brute-force attack has been analyzed under the new Palmprint protection scheme.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.
文摘In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.
