Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339054
Krzysztof Kryszczuk, J. Richiardi, A. Drygajlo
Biometric matching involves a comparison of two biometric data samples. In practical applications, one or both of the samples may be of degraded quality, in respect to the nominal quality of similar samples acquired in controlled conditions. It has been shown in prior art that in such situations, the integration of quality information into the process of bio-metric matching can lead to significantly improved classification performance of the biometric matcher. To facilitate such an integration, quality measures originating from both compared biometric samples are usually combined into one quality score. In this paper, we analyze the merit of doing so. We revisit the problem from a pattern classification perspective, and show that using individual quality measures as separate classification features frequently leads to a superior performance of a biometric system in comparison with the system in which quality measures are mapped into one quality score. We provide experimental support of this claim using synthetic data, as well as real biometric database, on the examples of face, fingerprint and multi-modal matching.
{"title":"Impact of combining quality measures on biometric sample matching","authors":"Krzysztof Kryszczuk, J. Richiardi, A. Drygajlo","doi":"10.1109/BTAS.2009.5339054","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339054","url":null,"abstract":"Biometric matching involves a comparison of two biometric data samples. In practical applications, one or both of the samples may be of degraded quality, in respect to the nominal quality of similar samples acquired in controlled conditions. It has been shown in prior art that in such situations, the integration of quality information into the process of bio-metric matching can lead to significantly improved classification performance of the biometric matcher. To facilitate such an integration, quality measures originating from both compared biometric samples are usually combined into one quality score. In this paper, we analyze the merit of doing so. We revisit the problem from a pattern classification perspective, and show that using individual quality measures as separate classification features frequently leads to a superior performance of a biometric system in comparison with the system in which quality measures are mapped into one quality score. We provide experimental support of this claim using synthetic data, as well as real biometric database, on the examples of face, fingerprint and multi-modal matching.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129462662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339060
E. Patterson, Amrutha Sethuram, K. Ricanek, Frederick J. Bingham
Normal adult aging in the face can drastically affect performance of face recognition systems. Synthetically generating age-progressed or age-regressed images for aiding recognizers is one method of improving the robustness of face-based biometrics. These synthetic age progressions may also aid human law enforcement and other applications. There has been wide interest in these techniques in recent years, and the use of Active Appearance Models (AAMs) for synthetic age progression has been shown to be a promising approach but has not yet been demonstrated on a large human population with wide variation. This paper presents improvements in AAM-based age progression that generate significantly improved visual results, taking into account a much wider gender, age, and ethnic range than published to date for age progression techniques.
{"title":"Improvements in Active Appearance Model based synthetic age progression for adult aging","authors":"E. Patterson, Amrutha Sethuram, K. Ricanek, Frederick J. Bingham","doi":"10.1109/BTAS.2009.5339060","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339060","url":null,"abstract":"Normal adult aging in the face can drastically affect performance of face recognition systems. Synthetically generating age-progressed or age-regressed images for aiding recognizers is one method of improving the robustness of face-based biometrics. These synthetic age progressions may also aid human law enforcement and other applications. There has been wide interest in these techniques in recent years, and the use of Active Appearance Models (AAMs) for synthetic age progression has been shown to be a promising approach but has not yet been demonstrated on a large human population with wide variation. This paper presents improvements in AAM-based age progression that generate significantly improved visual results, taking into account a much wider gender, age, and ethnic range than published to date for age progression techniques.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114152743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339033
N. Poh, A. Merati, J. Kittler
Cohort-based score normalization as examplified by the T-norm (for Test normalization) has been the state-of-the-art approach to account for the variability of signal quality in testing. On the other hand, user-specific score normalization such as the Z-norm and the F-norm, designed to handle variability in performance across different reference models, has also been shown to be very effective. Exploiting the strenghth of both approaches, this paper proposes a novel score normalization called adaptive F-norm, which is client-impostor centric, i.e., utilizing both the genuine and impostor score information, as well as adaptive, i.e, adaptive to the test condition thanks to the use of a pool of cohort models. Experiments based on the BioSecure DS2 database which contains 6 fingers of 415 subjects, each acquired using a thermal and an optical device, show that the proposed adaptive F-norm is better or at least as good as the other alternatives, including those recently proposed in the literature.
{"title":"Adaptive client-impostor centric score normalization: A case study in fingerprint verification","authors":"N. Poh, A. Merati, J. Kittler","doi":"10.1109/BTAS.2009.5339033","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339033","url":null,"abstract":"Cohort-based score normalization as examplified by the T-norm (for Test normalization) has been the state-of-the-art approach to account for the variability of signal quality in testing. On the other hand, user-specific score normalization such as the Z-norm and the F-norm, designed to handle variability in performance across different reference models, has also been shown to be very effective. Exploiting the strenghth of both approaches, this paper proposes a novel score normalization called adaptive F-norm, which is client-impostor centric, i.e., utilizing both the genuine and impostor score information, as well as adaptive, i.e, adaptive to the test condition thanks to the use of a pool of cohort models. Experiments based on the BioSecure DS2 database which contains 6 fingers of 415 subjects, each acquired using a thermal and an optical device, show that the proposed adaptive F-norm is better or at least as good as the other alternatives, including those recently proposed in the literature.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115175250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339062
N. Kalka, Nick Bartlow, B. Cukic
Arguably the most important task in iris recognition systems involves localization of the iris region of interest, a process known as iris segmentation. Research has found that segmentation results are a dominant factor that drives iris recognition matching performance. This work proposes techniques based on probabilistic intensity features and geometric features to arrive at scores indicating the success of both pupil and iris segmentation. The technique is fully automated and therefore requires no human supervision or manual evaluation. This work also presents a machine learning approach which utilizes the pupil and iris scores to arrive at an overall iris segmentation result prediction. We test the techniques using two iris segmentation algorithms of varying performance on two publicly available iris datasets. Our analysis shows that the approach is capable of arriving at segmentation scores suitable for predicting both the success and failure of pupil or iris segmentation. The proposed machine learning approach achieves an average classification accuracy of 98.45% across the four combinations of algorithms and datasets tested when predicting overall segmentation results. Finally, we present one potential application of the technique specific to iris match score performance and outline many other potential uses for the algorithm.
{"title":"An automated method for predicting iris segmentation failures","authors":"N. Kalka, Nick Bartlow, B. Cukic","doi":"10.1109/BTAS.2009.5339062","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339062","url":null,"abstract":"Arguably the most important task in iris recognition systems involves localization of the iris region of interest, a process known as iris segmentation. Research has found that segmentation results are a dominant factor that drives iris recognition matching performance. This work proposes techniques based on probabilistic intensity features and geometric features to arrive at scores indicating the success of both pupil and iris segmentation. The technique is fully automated and therefore requires no human supervision or manual evaluation. This work also presents a machine learning approach which utilizes the pupil and iris scores to arrive at an overall iris segmentation result prediction. We test the techniques using two iris segmentation algorithms of varying performance on two publicly available iris datasets. Our analysis shows that the approach is capable of arriving at segmentation scores suitable for predicting both the success and failure of pupil or iris segmentation. The proposed machine learning approach achieves an average classification accuracy of 98.45% across the four combinations of algorithms and datasets tested when predicting overall segmentation results. Finally, we present one potential application of the technique specific to iris match score performance and outline many other potential uses for the algorithm.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128639273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339073
Mayra Sacanamboy, B. Cukic
Biometric classification algorithms typically offer a range of performance characteristics which balance false non-match and false match rates. Nevertheless, the threshold which meets application requirements is usually selected without explicit consideration of cost implications of misclassification. This paper presents the analysis of recognition performance of multiple face and fingerprint algorithms using cost curves. Cost curves allow the introduction of misclassification costs and prior probabilities of proportions of genuine and impostor classes in the selection of biometric system thresholds. The inclusion of misclassification costs and prior probabilities is important since they can either change with time, or with the location where the biometric system is deployed.
{"title":"Cost curve analysis of biometric system performance","authors":"Mayra Sacanamboy, B. Cukic","doi":"10.1109/BTAS.2009.5339073","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339073","url":null,"abstract":"Biometric classification algorithms typically offer a range of performance characteristics which balance false non-match and false match rates. Nevertheless, the threshold which meets application requirements is usually selected without explicit consideration of cost implications of misclassification. This paper presents the analysis of recognition performance of multiple face and fingerprint algorithms using cost curves. Cost curves allow the introduction of misclassification costs and prior probabilities of proportions of genuine and impostor classes in the selection of biometric system thresholds. The inclusion of misclassification costs and prior probabilities is important since they can either change with time, or with the location where the biometric system is deployed.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133531540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339035
Nguyen van Huan, N. T. Binh, Hakil Kim
This paper introduces an eye-verifier for reliable detection of eyes in facial color images. At first, the eye region candidates are searched by the circular filter to the binary face image. The eye candidates are then fed into an eye-verifier. The eye-verifier uses a ternary template, generated from the eye area consisting of iris, sclera and skin. Then the template matching is made by ternary Hamming distance. Experimental results evaluating the proposed method on our own face database and FERET database show promising performance in terms of detection rate. The proposed algorithm showed a robust performance to the face poses and eye directions
{"title":"Eye-verifier using ternary template for reliable eye detection in facial color images","authors":"Nguyen van Huan, N. T. Binh, Hakil Kim","doi":"10.1109/BTAS.2009.5339035","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339035","url":null,"abstract":"This paper introduces an eye-verifier for reliable detection of eyes in facial color images. At first, the eye region candidates are searched by the circular filter to the binary face image. The eye candidates are then fed into an eye-verifier. The eye-verifier uses a ternary template, generated from the eye area consisting of iris, sclera and skin. Then the template matching is made by ternary Hamming distance. Experimental results evaluating the proposed method on our own face database and FERET database show promising performance in terms of detection rate. The proposed algorithm showed a robust performance to the face poses and eye directions","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121256912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339068
U. Park, A. Ross, Anil K. Jain
Periocular biometric refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric does not require high user cooperation and close capture distance unlike other ocular biometrics (e.g., iris, retina, and sclera). We study the feasibility of using periocular images of an individual as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set that can be used for matching. The effect of fusing these feature sets is also studied. The experimental results show a 77% rank-1 recognition accuracy using 958 images captured from 30 different subjects.
{"title":"Periocular biometrics in the visible spectrum: A feasibility study","authors":"U. Park, A. Ross, Anil K. Jain","doi":"10.1109/BTAS.2009.5339068","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339068","url":null,"abstract":"Periocular biometric refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric does not require high user cooperation and close capture distance unlike other ocular biometrics (e.g., iris, retina, and sclera). We study the feasibility of using periocular images of an individual as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set that can be used for matching. The effect of fusing these feature sets is also studied. The experimental results show a 77% rank-1 recognition accuracy using 958 images captured from 30 different subjects.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116000729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339075
Harini Jagadeesan, M. Hsiao
In the internet age, security is a major concern as identity thefts often cause detrimental effects. Masquerading is an important factor for identity theft and current authentication systems using traditional methods woefully lack mechanisms to detect and prevent it. This paper presents an application independent, continual, non-intrusive, fast and easily deployable user re-authentication system based on behavioral biometrics. These behavioral attributes are extracted from the keyboard and mouse operations of the user. They are used to identify and non-intrusively authenticate the user periodically. To extract suitable user attributes, we propose a novel heuristic that uses the percentage of mouse-to-keyboard interaction ratio and interaction quotient (IQ). In the re-authentication process, every time, the current behavior of the user is compared with the stored “expected” behavior. All deviations are noted and after a certain deviation threshold is reached, the system logs the user out of the current session. The underlying heuristic prevents imposters from misusing the system. Experimental results show that the proposed heuristic can greatly improve the accuracy of application-based and application independent systems to 96.4% and 82.2% respectively.
{"title":"A novel approach to design of user re-authentication systems","authors":"Harini Jagadeesan, M. Hsiao","doi":"10.1109/BTAS.2009.5339075","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339075","url":null,"abstract":"In the internet age, security is a major concern as identity thefts often cause detrimental effects. Masquerading is an important factor for identity theft and current authentication systems using traditional methods woefully lack mechanisms to detect and prevent it. This paper presents an application independent, continual, non-intrusive, fast and easily deployable user re-authentication system based on behavioral biometrics. These behavioral attributes are extracted from the keyboard and mouse operations of the user. They are used to identify and non-intrusively authenticate the user periodically. To extract suitable user attributes, we propose a novel heuristic that uses the percentage of mouse-to-keyboard interaction ratio and interaction quotient (IQ). In the re-authentication process, every time, the current behavior of the user is compared with the stored “expected” behavior. All deviations are noted and after a certain deviation threshold is reached, the system logs the user out of the current session. The underlying heuristic prevents imposters from misusing the system. Experimental results show that the proposed heuristic can greatly improve the accuracy of application-based and application independent systems to 96.4% and 82.2% respectively.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127523747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339016
Yasuhiro Tanabe, Kazuki Yoshizoe, H. Imai
We propose here a security evaluation methodology of image-based biometrics authentication systems against wolf attacks. A wolf attack is an attempt to impersonate a victim by feeding wolves into the system to be attacked. The wolf is input data that can be falsely accepted as a match with multiple templates. To create a secure system, we must evaluate the possibility of wolf attacks. Existing studies have relied on theoretical analysis of algorithms carried out by human beings, which is only effective if theoretical analysis is possible. Therefore, we propose a more generic approach based on a search to assist the developers of matching algorithms. We searched for wolves by using a recently developed algorithm called Monte-Carlo Tree Search (MCTS). We succeeded in detecting wolves in a matching algorithm, which appears promising considering that this is the first trial for this kind of approach.
{"title":"A study on security evaluation methodology for image-based biometrics authentication systems","authors":"Yasuhiro Tanabe, Kazuki Yoshizoe, H. Imai","doi":"10.1109/BTAS.2009.5339016","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339016","url":null,"abstract":"We propose here a security evaluation methodology of image-based biometrics authentication systems against wolf attacks. A wolf attack is an attempt to impersonate a victim by feeding wolves into the system to be attacked. The wolf is input data that can be falsely accepted as a match with multiple templates. To create a secure system, we must evaluate the possibility of wolf attacks. Existing studies have relied on theoretical analysis of algorithms carried out by human beings, which is only effective if theoretical analysis is possible. Therefore, we propose a more generic approach based on a search to assist the developers of matching algorithms. We searched for wolves by using a recently developed algorithm called Monte-Carlo Tree Search (MCTS). We succeeded in detecting wolves in a matching algorithm, which appears promising considering that this is the first trial for this kind of approach.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114199625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339015
D. Smeets, T. Fabry, Jeroen Hermans, D. Vandermeulen, P. Suetens
Currently, the recognition of faces under varying expressions is one of the main challenges in the face recognition community. In this paper a method is presented dealing with those expression variations by using an isometric deformation model. The method is built upon the geodesic distance matrix as a representation of the 3D face. We will show that the set of largest singular values is an excellent expression-invariant shape descriptor. Face comparison is performed by comparison of their shape descriptors using the mean normalized Manhattan distance as dissimilarity measure. The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal error rate of 13.37% for the verification scenario. This result is comparable with the equal error rates of other 3D expression-invariant face recognition methods using an isometric deformation model on the same database.
{"title":"Isometric deformation modeling using singular value decomposition for 3D expression-invariant face recognition","authors":"D. Smeets, T. Fabry, Jeroen Hermans, D. Vandermeulen, P. Suetens","doi":"10.1109/BTAS.2009.5339015","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339015","url":null,"abstract":"Currently, the recognition of faces under varying expressions is one of the main challenges in the face recognition community. In this paper a method is presented dealing with those expression variations by using an isometric deformation model. The method is built upon the geodesic distance matrix as a representation of the 3D face. We will show that the set of largest singular values is an excellent expression-invariant shape descriptor. Face comparison is performed by comparison of their shape descriptors using the mean normalized Manhattan distance as dissimilarity measure. The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal error rate of 13.37% for the verification scenario. This result is comparable with the equal error rates of other 3D expression-invariant face recognition methods using an isometric deformation model on the same database.","PeriodicalId":325900,"journal":{"name":"2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115823082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：