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
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
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
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
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
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
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
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
Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learnin...Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.展开更多
According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a ...According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a novel palmprint feature, which calledwavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energydistribution of the principal lines, wrinkles and ridges in different directions at differentresolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses thediscriminabilities of each level WEF and, according to these discriminabilities, chooses a suitableweight for each level to compute the weighted city block distance for recognition. The experimentalresults show that the order of the discriminabilities of each level WEF, from strong to weak, is the4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation andtranslation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprintverification and palmprint identification, respectively. These results demonstrate the power of theproposed approach.展开更多
Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition and have...Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results.In recent years,in the field of artificial intelligence,deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance.Some researchers have tried to use convolutional neural networks(CNNs)for palmprint recognition and palm vein recognition.However,the architectures of these CNNs have mostly been developed manually by human experts,which is a time-consuming and error-prone process.In order to overcome some shortcomings of manually designed CNN,neural architecture search(NAS)technology has become an important research direction of deep learning.The significance of NAS is to solve the deep learning model's parameter adjustment problem,which is a cross-study combining optimization and machine learning.NAS technology represents the future development direction of deep learning.However,up to now,NAS technology has not been well studied for palmprint recognition and palm vein recognition.In this paper,in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth,we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases,two palm vein databases,and one 3D palmprint database.Experimental results show that some NAS methods can achieve promising recognition results.Remarkably,among different evaluated NAS methods,Proxyless NAS achieves the best recognition performance.展开更多
Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have...Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have achieved impressive results.However,the research on deep learningbased palmprint recognition and palm vein recognition is still very preliminary.In this paper,in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition indepth,we conduct performance evaluation of seventeen representative and classic convolutional neural networks(CNNs)on one 3D palmprint database,five 2D palmprint databases and two palm vein databases.A lot of experiments have been carried out in the conditions of different network structures,different learning rates,and different numbers of network layers.We have also conducted experiments on both separate data mode and mixed data mode.Experimental results show that these classic CNNs can achieve promising recognition results,and the recognition performance of recently proposed CNNs is better.Particularly,among classic CNNs,one of the recently proposed classic CNNs,i.e.,EfficientNet achieves the best recognition accuracy.However,the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.展开更多
In this paper, a novel biometric identification system is presented toidentify a person''s identity by his/her palmprint. In contrast to existing palmprint systems forcriminal applications, the proposed system...In this paper, a novel biometric identification system is presented toidentify a person''s identity by his/her palmprint. In contrast to existing palmprint systems forcriminal applications, the proposed system targets at the civil applications, which requireidentifying a person in a large database with high accuracy in real-time. The system is constitutedby four major components: User Interface Module, Acquisition Module, Recognition Module and ExternalModule. More than 7,000 palmprint images have been collected to test the performance of the system.The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuineacceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017%and a false rejection rate, 0.86%. The execution time for the whole process including imagecollection, preprocessing, feature extraction and matching is less than 1 second.展开更多
Aiming at the problem of low recognition rate and poor security in the process of palmprint identity authentication,a cancelable palmprint template generating algorithm is proposed,which is based on local Gabor direct...Aiming at the problem of low recognition rate and poor security in the process of palmprint identity authentication,a cancelable palmprint template generating algorithm is proposed,which is based on local Gabor directional pattern with adaptive threshold by mean(m LGDP),difference local Gabor directional pattern with adaptive threshold by mean(m DLGDP)and feature fusion of them.In this method,the feature code of the image is segmented and the feature vectors are extracted and binarized.Then the Bloom filter is used to achieve many-toone mapping and the location scrambling of palmprint image.Finally,the scrambling result matrix and the user key are irreversibly transformed by the convolution operation to obtain a revocable template of the palmprint image.Both theoretical and experimental results analysis show that,in the case of key loss,the method of feature fusion can enhance the diversity of the original palmprint template effectively,improve the recognition rate efficiently,and have better security.展开更多
Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing method...Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.展开更多
This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PC...This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.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.
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 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.
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.
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.
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supported by National Science Foundation of China(No.62076086).
文摘Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.
文摘According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a novel palmprint feature, which calledwavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energydistribution of the principal lines, wrinkles and ridges in different directions at differentresolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses thediscriminabilities of each level WEF and, according to these discriminabilities, chooses a suitableweight for each level to compute the weighted city block distance for recognition. The experimentalresults show that the order of the discriminabilities of each level WEF, from strong to weak, is the4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation andtranslation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprintverification and palmprint identification, respectively. These results demonstrate the power of theproposed approach.
基金supported by National Science Foundation of China(Nos.62076086,61673157,61972129,61972127 and 61702154)Key Research and Development Program in Anhui Province(Nos.202004d07020008 and 201904d07020010)。
文摘Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results.In recent years,in the field of artificial intelligence,deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance.Some researchers have tried to use convolutional neural networks(CNNs)for palmprint recognition and palm vein recognition.However,the architectures of these CNNs have mostly been developed manually by human experts,which is a time-consuming and error-prone process.In order to overcome some shortcomings of manually designed CNN,neural architecture search(NAS)technology has become an important research direction of deep learning.The significance of NAS is to solve the deep learning model's parameter adjustment problem,which is a cross-study combining optimization and machine learning.NAS technology represents the future development direction of deep learning.However,up to now,NAS technology has not been well studied for palmprint recognition and palm vein recognition.In this paper,in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth,we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases,two palm vein databases,and one 3D palmprint database.Experimental results show that some NAS methods can achieve promising recognition results.Remarkably,among different evaluated NAS methods,Proxyless NAS achieves the best recognition performance.
基金National Science Foundation of China(Nos.61673157,62076086,61972129 and 61702154)Key Research and Development Program in Anhui Province(Nos.202004d07020008 and 201904d07020010).
文摘Palmprint recognition and palm vein recognition are two emerging biometrics technologies.In the past two decades,many traditional methods have been proposed for palmprint recognition and palm vein recognition,and have achieved impressive results.However,the research on deep learningbased palmprint recognition and palm vein recognition is still very preliminary.In this paper,in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition indepth,we conduct performance evaluation of seventeen representative and classic convolutional neural networks(CNNs)on one 3D palmprint database,five 2D palmprint databases and two palm vein databases.A lot of experiments have been carried out in the conditions of different network structures,different learning rates,and different numbers of network layers.We have also conducted experiments on both separate data mode and mixed data mode.Experimental results show that these classic CNNs can achieve promising recognition results,and the recognition performance of recently proposed CNNs is better.Particularly,among classic CNNs,one of the recently proposed classic CNNs,i.e.,EfficientNet achieves the best recognition accuracy.However,the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
文摘In this paper, a novel biometric identification system is presented toidentify a person''s identity by his/her palmprint. In contrast to existing palmprint systems forcriminal applications, the proposed system targets at the civil applications, which requireidentifying a person in a large database with high accuracy in real-time. The system is constitutedby four major components: User Interface Module, Acquisition Module, Recognition Module and ExternalModule. More than 7,000 palmprint images have been collected to test the performance of the system.The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuineacceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017%and a false rejection rate, 0.86%. The execution time for the whole process including imagecollection, preprocessing, feature extraction and matching is less than 1 second.
基金supported by the National Natural Science Foundation of China(61301091,61802302)the Scientific Research Program Funded by Shaanxi Provincial Education Department(18JK0717)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province of China(2015JQ6262)the Open Foundation of State Key Laboratory of Information Security(2015-MS-14)the New Star Team of Xi'an University of Posts and Telecommunications
文摘Aiming at the problem of low recognition rate and poor security in the process of palmprint identity authentication,a cancelable palmprint template generating algorithm is proposed,which is based on local Gabor directional pattern with adaptive threshold by mean(m LGDP),difference local Gabor directional pattern with adaptive threshold by mean(m DLGDP)and feature fusion of them.In this method,the feature code of the image is segmented and the feature vectors are extracted and binarized.Then the Bloom filter is used to achieve many-toone mapping and the location scrambling of palmprint image.Finally,the scrambling result matrix and the user key are irreversibly transformed by the convolution operation to obtain a revocable template of the palmprint image.Both theoretical and experimental results analysis show that,in the case of key loss,the method of feature fusion can enhance the diversity of the original palmprint template effectively,improve the recognition rate efficiently,and have better security.
基金We would like to thank the participants of the CAS_palm set who consented to participate in research.This project was funded by the Shanghai Municipal Science and Technology Major Project 2017SHZDZX01(S.W.)National Natural Science Foundation of China Grant 61831015(G.Z.)China Postdoctoral Science Foundation Grant 2019M651351(J.L.).
文摘Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.
基金supported fully by the TUBITAK Research Project under Grant No. 107E212.
文摘This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.