Pub Date : 2006-09-01DOI: 10.1109/BCC.2006.4341621
Yi Chen, Geppy Parziale, Eva Diaz-Santana, Anil K. Jain
Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise from wear and tear of surface coatings. A new generation of touchless live scan devices that generate 3D representation of fingerprints is appearing in the market. This new sensing technology addresses many of the problems stated above. However, 3D touchless fingerprint images need to be compatible with the legacy rolled images used in automated fingerprint identification systems (AFIS). In order to solve this interoperability issue, we propose an unwrapping algorithm that unfolds the 3D fingerprint in such a way that it resembles the effect of virtually rolling the 3D finger on a 2D plane. Our preliminary experiments show promising results in obtaining touchless fingerprint images that are of high quality and at the same time compatible with legacy rolled fingerprint images.
{"title":"3D Touchless Fingerprints: Compatibility with Legacy Rolled Images","authors":"Yi Chen, Geppy Parziale, Eva Diaz-Santana, Anil K. Jain","doi":"10.1109/BCC.2006.4341621","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341621","url":null,"abstract":"Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise from wear and tear of surface coatings. A new generation of touchless live scan devices that generate 3D representation of fingerprints is appearing in the market. This new sensing technology addresses many of the problems stated above. However, 3D touchless fingerprint images need to be compatible with the legacy rolled images used in automated fingerprint identification systems (AFIS). In order to solve this interoperability issue, we propose an unwrapping algorithm that unfolds the 3D fingerprint in such a way that it resembles the effect of virtually rolling the 3D finger on a 2D plane. Our preliminary experiments show promising results in obtaining touchless fingerprint images that are of high quality and at the same time compatible with legacy rolled fingerprint images.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116022390","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341618
R. Youmaran, Andy Adler
This paper develops a new approach to understand and measure variations in biometric sample quality. We begin with the intuition that degradations to a biometric sample will reduce the amount of identifiable information available. In order to measure the amount of identifiable information, we define biometric information as the decrease in uncertainty about the identity of a person due to a set of biometric measurements. We then show that the biometric information for a person may be calculated by the relative entropy D(p||q) between the population feature distribution q and the person's feature distribution p. The biometric information for a system is the mean D(p||q) for all persons in the population. In order to practically measure D(p||q) with limited data samples, we introduce an algorithm which regularizes a Gaussian model of the feature covariances. An example of this method is shown for PCA, Fisher linear discriminant (FLD) and ICA based face recognition, with biometric information calculated to be 45.0 bits (PCA), 37.0 bits (FLD), 39.0 bits (ICA) and 55.6 bits (fusion of PCA and FLD features). Based on this definition of biometric information, we simulate degradations of biometric images and calculate the resulting decrease in biometric information. Results show a quasi-linear decrease for small levels of blur with an asymptotic behavior at larger blur.
{"title":"Measuring Biometric Sample Quality in Terms of Biometric Information","authors":"R. Youmaran, Andy Adler","doi":"10.1109/BCC.2006.4341618","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341618","url":null,"abstract":"This paper develops a new approach to understand and measure variations in biometric sample quality. We begin with the intuition that degradations to a biometric sample will reduce the amount of identifiable information available. In order to measure the amount of identifiable information, we define biometric information as the decrease in uncertainty about the identity of a person due to a set of biometric measurements. We then show that the biometric information for a person may be calculated by the relative entropy D(p||q) between the population feature distribution q and the person's feature distribution p. The biometric information for a system is the mean D(p||q) for all persons in the population. In order to practically measure D(p||q) with limited data samples, we introduce an algorithm which regularizes a Gaussian model of the feature covariances. An example of this method is shown for PCA, Fisher linear discriminant (FLD) and ICA based face recognition, with biometric information calculated to be 45.0 bits (PCA), 37.0 bits (FLD), 39.0 bits (ICA) and 55.6 bits (fusion of PCA and FLD features). Based on this definition of biometric information, we simulate degradations of biometric images and calculate the resulting decrease in biometric information. Results show a quasi-linear decrease for small levels of blur with an asymptotic behavior at larger blur.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129789977","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341632
C. Barral, S. Vaudenay
The concept of match-on-card (MoC) consists of a smart card which receives an applicant's candidate template T to be compared with the stored reference template Tref by processing the complete matching algorithm during a biometric authentication request. The smart card will then output whether this comparison is positive or not. The main argument against MoC-enabled smart cards is that it opens the way for YesCard (i.e. an attack path previously seen in banking, a card always returning "yes"). The threat regarding biometrics is not only YesCard, but also NoCard as we will see in this paper. We will propose a protocol to easily thwart these attacks by using simple cryptographic primitives such as symmetric encryption. This protocol will however only protect the system from malicious smart cards, but will not protect the smart card against malicious systems. Finally we will enhance this pro tocol to protect the smart card against its use as a so-called oracle to guess the stored reference biometric template.
{"title":"A Protection Scheme for MOC-Enabled Smart Cards","authors":"C. Barral, S. Vaudenay","doi":"10.1109/BCC.2006.4341632","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341632","url":null,"abstract":"The concept of match-on-card (MoC) consists of a smart card which receives an applicant's candidate template T to be compared with the stored reference template Tref by processing the complete matching algorithm during a biometric authentication request. The smart card will then output whether this comparison is positive or not. The main argument against MoC-enabled smart cards is that it opens the way for YesCard (i.e. an attack path previously seen in banking, a card always returning \"yes\"). The threat regarding biometrics is not only YesCard, but also NoCard as we will see in this paper. We will propose a protocol to easily thwart these attacks by using simple cryptographic primitives such as symmetric encryption. This protocol will however only protect the system from malicious smart cards, but will not protect the smart card against malicious systems. Finally we will enhance this pro tocol to protect the smart card against its use as a so-called oracle to guess the stored reference biometric template.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121536062","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341634
S. Tulyakov, V. Govindaraju
This paper considers combinations of biometric matchers in identification system. We assume that the test template is matched not only against the enrolled template of claimed person identity, but also against few enrolled templates of other persons, and all matching scores are available to the combination algorithm. We present a combination method utilizing the dependencies between these scores and showing better performance than comparable traditional combination method using only matching scores related to the claimed identity.
{"title":"Identification Model for Classifier Combinations","authors":"S. Tulyakov, V. Govindaraju","doi":"10.1109/BCC.2006.4341634","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341634","url":null,"abstract":"This paper considers combinations of biometric matchers in identification system. We assume that the test template is matched not only against the enrolled template of claimed person identity, but also against few enrolled templates of other persons, and all matching scores are available to the combination algorithm. We present a combination method utilizing the dependencies between these scores and showing better performance than comparable traditional combination method using only matching scores related to the claimed identity.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130565217","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341619
T. Polon, S. Sander
Migration from password and token-based authentication in distributed systems requires fundamental changes to the authentication process. A person's biometric data is not a secret, which presents a fundamental difference with other authentication methods. Matching a sample with a database template is secondary to establishing trust in the integrity of the sample. The process is similar to establishing a chain of custody for judicial evidence. In computer systems this is accomplished using attestation architectures. In this paper, a design for a secure remote biometric login system based on an attestation architecture is analyzed. The system uses a commercially available Trusted Platform Module (TPM) to authenticate the platform during the boot process and perform trusted private-key functions to participate in a challenge/response between the client and a remote biometric matcher. The result is a system that can provide higher assurance than current systems in an economically and administratively feasible system.
{"title":"Attestation-Based Remote Biometric Authentication","authors":"T. Polon, S. Sander","doi":"10.1109/BCC.2006.4341619","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341619","url":null,"abstract":"Migration from password and token-based authentication in distributed systems requires fundamental changes to the authentication process. A person's biometric data is not a secret, which presents a fundamental difference with other authentication methods. Matching a sample with a database template is secondary to establishing trust in the integrity of the sample. The process is similar to establishing a chain of custody for judicial evidence. In computer systems this is accomplished using attestation architectures. In this paper, a design for a secure remote biometric login system based on an attestation architecture is analyzed. The system uses a commercially available Trusted Platform Module (TPM) to authenticate the platform during the boot process and perform trusted private-key functions to participate in a challenge/response between the client and a remote biometric matcher. The result is a system that can provide higher assurance than current systems in an economically and administratively feasible system.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120948663","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341617
R. Hsu, J. Shah, B. Martin
Quality assessment of facial images differs from the traditional quality assessment of image and video signals with regards to its multiple goals such as to ensure its fidelity to the human visual system (HVS) model, to predict matching performance, to generate feedback on image acquisition, to guard the enrollment process, and to provide a weight for merging multimodal biometrics. In this paper, we present a quality assessment framework that complies with the requirements of ISO/IEC 19794-5 for facial biometrics and additionally ensures optimal recognition performance. This framework employs a novel classification-based score normalization process for various quality metrics and includes techniques to fuse those individual quality scores into an overall quality score which is shown to be correlated to the genuine match scores of the Facelt face recognition engine. We confirm the effectiveness of this overall quality score at satisfying multiple goals by first parameterizing ROC curves with average database quality to show the predictive nature of the metric and secondly by showing the consistency between this overall quality score and human perception of image quality.
{"title":"Quality Assessment of Facial Images","authors":"R. Hsu, J. Shah, B. Martin","doi":"10.1109/BCC.2006.4341617","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341617","url":null,"abstract":"Quality assessment of facial images differs from the traditional quality assessment of image and video signals with regards to its multiple goals such as to ensure its fidelity to the human visual system (HVS) model, to predict matching performance, to generate feedback on image acquisition, to guard the enrollment process, and to provide a weight for merging multimodal biometrics. In this paper, we present a quality assessment framework that complies with the requirements of ISO/IEC 19794-5 for facial biometrics and additionally ensures optimal recognition performance. This framework employs a novel classification-based score normalization process for various quality metrics and includes techniques to fuse those individual quality scores into an overall quality score which is shown to be correlated to the genuine match scores of the Facelt face recognition engine. We confirm the effectiveness of this overall quality score at satisfying multiple goals by first parameterizing ROC curves with average database quality to show the predictive nature of the metric and secondly by showing the consistency between this overall quality score and human perception of image quality.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134410560","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341620
J. Hube
We show how to estimate identification performance for arbitrary gallery size from an ROC curve. The method presented can: (1) estimate an alarm curve; (2) give rank 1 dependence on gallery size; and (3) estimate a CMC curve. We show that the method assumes consistently normalized scores, with normalization the degree of freedom defining the connection between identification and verification. A measure to quantify normalization consistency is defined. Examples are given using both fingerprint and face databases. Further examples are based on the results given in the FRVT 2002 evaluation report.
{"title":"Using Biometric Verification to Estimate Identification Performance","authors":"J. Hube","doi":"10.1109/BCC.2006.4341620","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341620","url":null,"abstract":"We show how to estimate identification performance for arbitrary gallery size from an ROC curve. The method presented can: (1) estimate an alarm curve; (2) give rank 1 dependence on gallery size; and (3) estimate a CMC curve. We show that the method assumes consistently normalized scores, with normalization the degree of freedom defining the connection between identification and verification. A measure to quantify normalization consistency is defined. Examples are given using both fingerprint and face databases. Further examples are based on the results given in the FRVT 2002 evaluation report.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114318580","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341613
Haiping Lu, K. Plataniotis, A. Venetsanopoulos
This paper solves the gait recognition problem in a multilinear principal component analysis (MPCA) framework. Gait sequences are naturally described as tensor objects and feature extraction for tensor objects is important in computer vision and pattern recognition applications. Classical principal component analysis (PCA) operates on vectors and it is not directly applicable to gait sequences. This work introduces an MPCA framework for feature extraction from gait sequences by seeking a multilinear projection onto a tensor subspace of lower dimensionality which captures most of the variance of the original gait samples. A subset of the extracted eigen-tensors are selected and the classical LDA is then applied. In experiments, gait recognition results are reported on the Gait Challenge data sets using the proposed solution. The results indicate that with a simple design, the proposed algorithm outperforms the state-of-the-art algorithms.
{"title":"Gait Recognition Through MPCA Plus LDA","authors":"Haiping Lu, K. Plataniotis, A. Venetsanopoulos","doi":"10.1109/BCC.2006.4341613","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341613","url":null,"abstract":"This paper solves the gait recognition problem in a multilinear principal component analysis (MPCA) framework. Gait sequences are naturally described as tensor objects and feature extraction for tensor objects is important in computer vision and pattern recognition applications. Classical principal component analysis (PCA) operates on vectors and it is not directly applicable to gait sequences. This work introduces an MPCA framework for feature extraction from gait sequences by seeking a multilinear projection onto a tensor subspace of lower dimensionality which captures most of the variance of the original gait samples. A subset of the extracted eigen-tensors are selected and the classical LDA is then applied. In experiments, gait recognition results are reported on the Gait Challenge data sets using the proposed solution. The results indicate that with a simple design, the proposed algorithm outperforms the state-of-the-art algorithms.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706218","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341628
D. Hatzinakos
Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric, the human heartbeat, can be used for identity recognition. Existing approaches address the problem by using electrocardiogram (ECG) data and the fiducials of the different parts of the heartrate. However, the current fiducial detection tools are inadequate for this application since the boundaries of waveforms are difficult to detect, locate and define. In this paper, an ECG biometric recognition method that does not require any waveform detections is introduced based on classification of coefficients from the discrete cosine transform (DCT) of the Autocorrelation (AC) sequence of ECG data segments. Low false negative rates, low false positive rates and a 100% subject recognition rate for healthy subjects can be achieved for parameters that are suitable for the database.
{"title":"ECG Biometric Recognition Without Fiducial Detection","authors":"D. Hatzinakos","doi":"10.1109/BCC.2006.4341628","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341628","url":null,"abstract":"Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric, the human heartbeat, can be used for identity recognition. Existing approaches address the problem by using electrocardiogram (ECG) data and the fiducials of the different parts of the heartrate. However, the current fiducial detection tools are inadequate for this application since the boundaries of waveforms are difficult to detect, locate and define. In this paper, an ECG biometric recognition method that does not require any waveform detections is introduced based on classification of coefficients from the discrete cosine transform (DCT) of the Autocorrelation (AC) sequence of ECG data segments. Low false negative rates, low false positive rates and a 100% subject recognition rate for healthy subjects can be achieved for parameters that are suitable for the database.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126315345","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 : 2006-09-01DOI: 10.1109/BCC.2006.4341636
S. Shah, A. Abaza, A. Ross, H. Ammar
Automating the postmortem identification of deceased individuals based on dental characteristics is receiving increased attention especially with the large number of victims encountered in mass disasters. An automated dental identification system compares the teeth present in multiple digitized dental records in order to access their similarity. The primary step in such a system is the estimation of the contour of each tooth in order to permit efficient feature extraction. Extracting the contour of the teeth is a very challenging task and has received inadequate attention in the literature. In this paper, the task of teeth contour extraction is accomplished using active contour without edges. This technique is based on the intensity of the overall region of the tooth image and, therefore, does not necessitate the presence of a sharp boundary between teeth. Further, this technique can extract the region contour in the presence of additive noise and in the absence of well-defined image gradients. Experimental results indicate the benefits of the proposed approach.
{"title":"Automatic Tooth Segmentation Using Active Contour Without Edges","authors":"S. Shah, A. Abaza, A. Ross, H. Ammar","doi":"10.1109/BCC.2006.4341636","DOIUrl":"https://doi.org/10.1109/BCC.2006.4341636","url":null,"abstract":"Automating the postmortem identification of deceased individuals based on dental characteristics is receiving increased attention especially with the large number of victims encountered in mass disasters. An automated dental identification system compares the teeth present in multiple digitized dental records in order to access their similarity. The primary step in such a system is the estimation of the contour of each tooth in order to permit efficient feature extraction. Extracting the contour of the teeth is a very challenging task and has received inadequate attention in the literature. In this paper, the task of teeth contour extraction is accomplished using active contour without edges. This technique is based on the intensity of the overall region of the tooth image and, therefore, does not necessitate the presence of a sharp boundary between teeth. Further, this technique can extract the region contour in the presence of additive noise and in the absence of well-defined image gradients. Experimental results indicate the benefits of the proposed approach.","PeriodicalId":226152,"journal":{"name":"2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792531","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：