The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision...The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).展开更多
This paper presents an algorithm and then MATLAB program that can construct and process an image depending on a given database and face recognition technique, which helps in the operations of investigation issues for ...This paper presents an algorithm and then MATLAB program that can construct and process an image depending on a given database and face recognition technique, which helps in the operations of investigation issues for policemen and in any similar operations, the image gets constructed and implemented as the database is developed. It is found that such image processing operation helps in operations needs quick investigation transactions of some issues like policemen works and operations. The method depends on the given database about the face of the person, the face recognition depends on drawing a face of the given data and then comparing the resulted face with the stored data and find the most closes one and choose it to be its goal. This operation needs a time, it is not real-time operation but the time needed is too short. This method develop a method to make the operation of searching about some unknown person or face faster which helps more all sectors interested in searching about some unknowns in their transactions.展开更多
Through the online survey of 100 samples of the domestic library consortia, this paper analyzes and compares the number of the self-built databases of the library consortia, the standards and specifications of buildin...Through the online survey of 100 samples of the domestic library consortia, this paper analyzes and compares the number of the self-built databases of the library consortia, the standards and specifications of building the databases, the contents of the databases, the sharing principles, the management mechanisms and the effectiveness of the result displaying, and points out the problems existing in the self-built databases of the domestic library consortia and puts forward the countermeasures.展开更多
Cancellable biometrics is the solution for the trade-off between two concepts:Biometrics for Security and Security for Biometrics.The cancelable template is stored in the authentication system’s database rather than ...Cancellable biometrics is the solution for the trade-off between two concepts:Biometrics for Security and Security for Biometrics.The cancelable template is stored in the authentication system’s database rather than the original biometric data.In case of the database is compromised,it is easy for the template to be canceled and regenerated from the same biometric data.Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters(cancelable key).Therefore,the cancelable key must be secret to be used in the system authentication process as a second authentication factor in con-junction with the biometric data.The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait(in different feature domains)as the only authentication factor,in addition to achieving good performance with high security.The standard Generative Adversarial Network(GAN)is proposed as an encryption tool that needs the cancelable key during the training phase,and the testing phase depends only on the biometric trait.Additionally,random projection transformation is employed to increase the proposed system’s security and performance.The proposed transformation system is tested using the standard ORL face database,and the experiments are done by applying different features domains.Moreover,a security analysis for the proposed transformation system is presented.展开更多
Taxonomic names are key links between various databases that store information on different organisms.Several global fungal nomenclural and taxonomic databases(notably Index Fungorum,Species Fungorum and MycoBank)can ...Taxonomic names are key links between various databases that store information on different organisms.Several global fungal nomenclural and taxonomic databases(notably Index Fungorum,Species Fungorum and MycoBank)can be sourced to find taxonomic details about fungi,while DNA sequence data can be sourced from NCBI,EBI and UNITE databases.Although the sequence data may be linked to a name,the quality of the metadata is variable and generally there is no corresponding link to images,descriptions or herbarium material.There is generally no way to establish the accuracy of the names in these genomic databases,other than whether the submission is from a reputable source.To tackle this problem,a new database(FacesofFungi),accessible at www.facesoffungi.org(FoF)has been established.This fungal database allows deposition of taxonomic data,phenotypic details and other useful data,which will enhance our current taxonomic understanding and ultimately enable mycologists to gain better and updated insights into the current fungal classification system.In addition,the database will also allow access to comprehensive metadata including descriptions of voucher and type specimens.This database is user-friendly,providing links and easy access between taxonomic ranks,with the classification system based primarily on molecular data(from the literature and via updated web-based phylogenetic trees),and to a lesser extent on morphological data when molecular data are unavailable.In FoF species are not only linked to the closest phylogenetic representatives,but also relevant data is provided,wherever available,on various applied aspects,such as ecological,industrial,quarantine and chemical uses.The data include the three main fungal groups(Ascomycota,Basidiomycota,Basal fungi)and fungus-like organisms.The FoF webpage is an output funded by the Mushroom Research Foundation which is an NGO with seven directors with mycological expertise.The webpage has 76 curators,and with the help of these specialists,FoF will provide an updated natural classification of the fungi,with illustrated accounts of species linked to molecular data.The present paper introduces the FoF database to the scientific community and briefly reviews some of the problems associated with classification and identification of the main fungal groups.The structure and use of the database is then explained.We would like to invite all mycologists to contribute to these web pages.展开更多
基金supported and funded by KAU Scientific Endowment,King Abdulaziz University,Jeddah,Saudi Arabia,grant number 077416-04.
文摘The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).
文摘This paper presents an algorithm and then MATLAB program that can construct and process an image depending on a given database and face recognition technique, which helps in the operations of investigation issues for policemen and in any similar operations, the image gets constructed and implemented as the database is developed. It is found that such image processing operation helps in operations needs quick investigation transactions of some issues like policemen works and operations. The method depends on the given database about the face of the person, the face recognition depends on drawing a face of the given data and then comparing the resulted face with the stored data and find the most closes one and choose it to be its goal. This operation needs a time, it is not real-time operation but the time needed is too short. This method develop a method to make the operation of searching about some unknown person or face faster which helps more all sectors interested in searching about some unknowns in their transactions.
文摘Through the online survey of 100 samples of the domestic library consortia, this paper analyzes and compares the number of the self-built databases of the library consortia, the standards and specifications of building the databases, the contents of the databases, the sharing principles, the management mechanisms and the effectiveness of the result displaying, and points out the problems existing in the self-built databases of the domestic library consortia and puts forward the countermeasures.
文摘Cancellable biometrics is the solution for the trade-off between two concepts:Biometrics for Security and Security for Biometrics.The cancelable template is stored in the authentication system’s database rather than the original biometric data.In case of the database is compromised,it is easy for the template to be canceled and regenerated from the same biometric data.Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters(cancelable key).Therefore,the cancelable key must be secret to be used in the system authentication process as a second authentication factor in con-junction with the biometric data.The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait(in different feature domains)as the only authentication factor,in addition to achieving good performance with high security.The standard Generative Adversarial Network(GAN)is proposed as an encryption tool that needs the cancelable key during the training phase,and the testing phase depends only on the biometric trait.Additionally,random projection transformation is employed to increase the proposed system’s security and performance.The proposed transformation system is tested using the standard ORL face database,and the experiments are done by applying different features domains.Moreover,a security analysis for the proposed transformation system is presented.
基金Wen are grateful to The National Natural Science Foundation of China(No.31460012,No.31200016).
文摘Taxonomic names are key links between various databases that store information on different organisms.Several global fungal nomenclural and taxonomic databases(notably Index Fungorum,Species Fungorum and MycoBank)can be sourced to find taxonomic details about fungi,while DNA sequence data can be sourced from NCBI,EBI and UNITE databases.Although the sequence data may be linked to a name,the quality of the metadata is variable and generally there is no corresponding link to images,descriptions or herbarium material.There is generally no way to establish the accuracy of the names in these genomic databases,other than whether the submission is from a reputable source.To tackle this problem,a new database(FacesofFungi),accessible at www.facesoffungi.org(FoF)has been established.This fungal database allows deposition of taxonomic data,phenotypic details and other useful data,which will enhance our current taxonomic understanding and ultimately enable mycologists to gain better and updated insights into the current fungal classification system.In addition,the database will also allow access to comprehensive metadata including descriptions of voucher and type specimens.This database is user-friendly,providing links and easy access between taxonomic ranks,with the classification system based primarily on molecular data(from the literature and via updated web-based phylogenetic trees),and to a lesser extent on morphological data when molecular data are unavailable.In FoF species are not only linked to the closest phylogenetic representatives,but also relevant data is provided,wherever available,on various applied aspects,such as ecological,industrial,quarantine and chemical uses.The data include the three main fungal groups(Ascomycota,Basidiomycota,Basal fungi)and fungus-like organisms.The FoF webpage is an output funded by the Mushroom Research Foundation which is an NGO with seven directors with mycological expertise.The webpage has 76 curators,and with the help of these specialists,FoF will provide an updated natural classification of the fungi,with illustrated accounts of species linked to molecular data.The present paper introduces the FoF database to the scientific community and briefly reviews some of the problems associated with classification and identification of the main fungal groups.The structure and use of the database is then explained.We would like to invite all mycologists to contribute to these web pages.
