Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339022
K. Radhika, M. K. Venkatesha, G N Shekar
Zernike moments are image descriptors often used in pattern recognition. They offer rotation invariance. In this paper, we discuss a novel method of signature authentication using Zernike moments. Instead of working on primary features such as image or on-line data, working on the derived kinematic plot is a robust way of authentication. The derived kinematic plot considered in this paper is acceleration plot. Each signature's on-line acceleration information is being weighted by Zernike moment. The shape analysis of the acceleration plot, using only lower order Zernike moments is performed for authentication of on-line signature.
{"title":"On-line signature authentication using Zernike moments","authors":"K. Radhika, M. K. Venkatesha, G N Shekar","doi":"10.1109/BTAS.2009.5339022","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339022","url":null,"abstract":"Zernike moments are image descriptors often used in pattern recognition. They offer rotation invariance. In this paper, we discuss a novel method of signature authentication using Zernike moments. Instead of working on primary features such as image or on-line data, working on the derived kinematic plot is a robust way of authentication. The derived kinematic plot considered in this paper is acceleration plot. Each signature's on-line acceleration information is being weighted by Zernike moment. The shape analysis of the acceleration plot, using only lower order Zernike moments is performed for authentication of on-line signature.","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":"125339227","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.2009.5339046
E. Kelkboom, K.T.J. de Groot, C. Chen, J. Breebaart, R. Veldhuis
One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms.
{"title":"Pitfall of the Detection Rate Optimized Bit Allocation within template protection and a remedy","authors":"E. Kelkboom, K.T.J. de Groot, C. Chen, J. Breebaart, R. Veldhuis","doi":"10.1109/BTAS.2009.5339046","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339046","url":null,"abstract":"One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms.","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":"114722816","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.2009.5339053
Khoa Luu, K. Ricanek, T. D. Bui, C. Suen
In this paper, we introduce a novel age estimation technique that combines Active Appearance Models (AAMs) and Support Vector Machines (SVMs), to dramatically improve the accuracy of age estimation over the current state-of-the-art techniques. In this method, characteristics of the input images, face image, are interpreted as feature vectors by AAMs, which are used to discriminate between childhood and adulthood, prior to age estimation. Faces classified as adults are passed to the adult age-determination function and the others are passed to the child age-determination function. Compared to published results, this method yields the highest accuracy recognition rates, both in overall mean-absolute error (MAE) and mean-absolute error for the two periods of human development: childhood and adulthood.
{"title":"Age estimation using Active Appearance Models and Support Vector Machine regression","authors":"Khoa Luu, K. Ricanek, T. D. Bui, C. Suen","doi":"10.1109/BTAS.2009.5339053","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339053","url":null,"abstract":"In this paper, we introduce a novel age estimation technique that combines Active Appearance Models (AAMs) and Support Vector Machines (SVMs), to dramatically improve the accuracy of age estimation over the current state-of-the-art techniques. In this method, characteristics of the input images, face image, are interpreted as feature vectors by AAMs, which are used to discriminate between childhood and adulthood, prior to age estimation. Faces classified as adults are passed to the adult age-determination function and the others are passed to the child age-determination function. Compared to published results, this method yields the highest accuracy recognition rates, both in overall mean-absolute error (MAE) and mean-absolute error for the two periods of human development: childhood and adulthood.","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":"124515050","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.2009.5339023
S. Shafaei, T. Inanc, L. Hassebrook
For many years, fingerprints have been captured by pressing a finger against a paper or hard surface. This touch-based fingerprint acquisition introduces some problems such as distortions and deformations in the acquired images, which arise due to the contact of the fingerprint surface with the sensor platen, and degrades the recognition performance. A new touch-less fingerprint technology has been recently introduced to the market, which can address the problems with the contact-based fingerprint systems. In this paper, we propose a new algorithm for unwrapping the acquired 3-D scan of the subject's finger into a 2-D rolled equivalent image. Therefore, The resulting image can be matched with the conventional 2-D scans; it also can be used for matching unwrapped 3-D fingerprints among themselves with the 2-D fingerprint matching algorithms. The algorithm is based on curvature analysis of the 3-D surface. The quality of the resulting image is evaluated and analyzed using NIST fingerprint image software.
{"title":"A new approach to unwrap a 3-D fingerprint to a 2-D rolled equivalent fingerprint","authors":"S. Shafaei, T. Inanc, L. Hassebrook","doi":"10.1109/BTAS.2009.5339023","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339023","url":null,"abstract":"For many years, fingerprints have been captured by pressing a finger against a paper or hard surface. This touch-based fingerprint acquisition introduces some problems such as distortions and deformations in the acquired images, which arise due to the contact of the fingerprint surface with the sensor platen, and degrades the recognition performance. A new touch-less fingerprint technology has been recently introduced to the market, which can address the problems with the contact-based fingerprint systems. In this paper, we propose a new algorithm for unwrapping the acquired 3-D scan of the subject's finger into a 2-D rolled equivalent image. Therefore, The resulting image can be matched with the conventional 2-D scans; it also can be used for matching unwrapped 3-D fingerprints among themselves with the 2-D fingerprint matching algorithms. The algorithm is based on curvature analysis of the 3-D surface. The quality of the resulting image is evaluated and analyzed using NIST fingerprint image software.","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":"126061209","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.2009.5339038
R. Abiantun, M. Savvides
In this paper, we briefly review AdaBoost and expand on the Discrete version by building weak classifiers from a pair of biased classifiers which enable the weak classifier to abstain from classifying some samples. We show that this approach turns into a 3-bin Real AdaBoost approach where the bin sizes and positions are set by the bias parameters selected by the user and dynamically change with every iteration which make it different from the traditional Real AdaBoost. We apply this method to face detection more specifically the Viola-Jones approach to detecting faces with Haar-like features and empirically show that our method can help improving the generalization ability by reducing the testing error of the final classifier. We benchmark the results on the MIT+CMU database.
{"title":"Dynamic three-bin real AdaBoost using biased classifiers: An application in face detection","authors":"R. Abiantun, M. Savvides","doi":"10.1109/BTAS.2009.5339038","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339038","url":null,"abstract":"In this paper, we briefly review AdaBoost and expand on the Discrete version by building weak classifiers from a pair of biased classifiers which enable the weak classifier to abstain from classifying some samples. We show that this approach turns into a 3-bin Real AdaBoost approach where the bin sizes and positions are set by the bias parameters selected by the user and dynamically change with every iteration which make it different from the traditional Real AdaBoost. We apply this method to face detection more specifically the Viola-Jones approach to detecting faces with Haar-like features and empirically show that our method can help improving the generalization ability by reducing the testing error of the final classifier. We benchmark the results on the MIT+CMU 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":"129235667","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.2009.5339067
Jaishanker K. Pillai, Vishal M. Patel, R. Chellappa
Iris images acquired from a partially cooperating subject often suffer from blur, occlusion due to eyelids, and specular reflections. The performance of existing iris recognition systems degrade significantly on these images. Hence it is essential to select good images from the incoming iris video stream, before they are input to the recognition algorithm. In this paper, we propose a sparsity based algorithm for selection of good iris images and their subsequent recognition. Unlike most existing algorithms for iris image selection, our method can handle segmentation errors and a wider range of acquisition artifacts common in iris image capture. We perform selection and recognition in a single step which is more efficient than devising separate specialized algorithms for the two. Recognition from partially cooperating users is a significant step towards deploying iris systems in a wide variety of applications.
{"title":"Sparsity inspired selection and recognition of iris images","authors":"Jaishanker K. Pillai, Vishal M. Patel, R. Chellappa","doi":"10.1109/BTAS.2009.5339067","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339067","url":null,"abstract":"Iris images acquired from a partially cooperating subject often suffer from blur, occlusion due to eyelids, and specular reflections. The performance of existing iris recognition systems degrade significantly on these images. Hence it is essential to select good images from the incoming iris video stream, before they are input to the recognition algorithm. In this paper, we propose a sparsity based algorithm for selection of good iris images and their subsequent recognition. Unlike most existing algorithms for iris image selection, our method can handle segmentation errors and a wider range of acquisition artifacts common in iris image capture. We perform selection and recognition in a single step which is more efficient than devising separate specialized algorithms for the two. Recognition from partially cooperating users is a significant step towards deploying iris systems in a wide variety of applications.","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":"131461147","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.2009.5339039
Ramachandra Raghavendra, B. Dorizzi, A. Rao, G. Hemantha
In this paper, we present an efficient feature level fusion scheme that we apply on face and palmprint images. The features for each modality are obtained using Log Gabor transform and concatenated to form a fused feature vector. We then use Particle Swarm Optimization (PSO) scheme to reduce the dimension of this vector. Final classification is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on a virtual multimodal biometric database of 250 users built from the face FRGC and the palmprint PolyU databases. We compare the proposed selection method with the well known Adaptive Boosting (AdaBoost) method in terms of both number of features selected and performance. Experimental results in both closed identification and verification rates show that feature fusion improves performance over match score level fusion and also that the proposed method outperforms AdaBoost in terms of reduction of the number of features and facility of implementation.
{"title":"PSO versus AdaBoost for feature selection in multimodal biometrics","authors":"Ramachandra Raghavendra, B. Dorizzi, A. Rao, G. Hemantha","doi":"10.1109/BTAS.2009.5339039","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339039","url":null,"abstract":"In this paper, we present an efficient feature level fusion scheme that we apply on face and palmprint images. The features for each modality are obtained using Log Gabor transform and concatenated to form a fused feature vector. We then use Particle Swarm Optimization (PSO) scheme to reduce the dimension of this vector. Final classification is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on a virtual multimodal biometric database of 250 users built from the face FRGC and the palmprint PolyU databases. We compare the proposed selection method with the well known Adaptive Boosting (AdaBoost) method in terms of both number of features selected and performance. Experimental results in both closed identification and verification rates show that feature fusion improves performance over match score level fusion and also that the proposed method outperforms AdaBoost in terms of reduction of the number of features and facility of implementation.","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":"124416089","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.2009.5339052
Przemyslaw Szeptycki, M. Ardabilian, Liming Chen
Automatic 2.5D face landmarking aims at locating facial feature points on 2.5D face models, such as eye corners, nose tip, etc. and has many applications ranging from face registration to facial expression recognition. In this paper, we propose a rotation invariant 2.5D face landmarking solution based on facial curvature analysis combined with a generic 2.5D face model and make use of a coarse-to-fine strategy for more accurate facial feature points localization. Experimented on more than 1600 face models randomly selected from the FRGC dataset, our technique displays, compared to a ground truth from a manual 3D face landmarking, a 100% of good nose tip localization in 8 mm precision and 100% of good localization for the eye inner corner in 12 mm precision.
{"title":"A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking","authors":"Przemyslaw Szeptycki, M. Ardabilian, Liming Chen","doi":"10.1109/BTAS.2009.5339052","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339052","url":null,"abstract":"Automatic 2.5D face landmarking aims at locating facial feature points on 2.5D face models, such as eye corners, nose tip, etc. and has many applications ranging from face registration to facial expression recognition. In this paper, we propose a rotation invariant 2.5D face landmarking solution based on facial curvature analysis combined with a generic 2.5D face model and make use of a coarse-to-fine strategy for more accurate facial feature points localization. Experimented on more than 1600 face models randomly selected from the FRGC dataset, our technique displays, compared to a ground truth from a manual 3D face landmarking, a 100% of good nose tip localization in 8 mm precision and 100% of good localization for the eye inner corner in 12 mm precision.","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":"132276331","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.2009.5339042
Foteini Agrafioti, F. Bui, D. Hatzinakos
The electrocardiogram (ECG) is a medical signal that has lately drawn interest from the biometrics community, and has been shown to have significantly discriminative characteristics in a population. This paper brings to light the particular challenges of electrocardiogram recognition to advocate that time dependency is a controversial point. In contrast to traditional biometrics, ECG allows for continuous authentication and consequently expands the range of applications. However, time varying biometrics put on the line the recognition accuracy due to increased intra subject variability. This paper suggests a novel framework for bypassing this inadequacy. A template update methodology is proposed and demonstrated to boost the recognition performance over 2 hour recordings of 10 subjects.
{"title":"Medical biometrics: The perils of ignoring time dependency","authors":"Foteini Agrafioti, F. Bui, D. Hatzinakos","doi":"10.1109/BTAS.2009.5339042","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339042","url":null,"abstract":"The electrocardiogram (ECG) is a medical signal that has lately drawn interest from the biometrics community, and has been shown to have significantly discriminative characteristics in a population. This paper brings to light the particular challenges of electrocardiogram recognition to advocate that time dependency is a controversial point. In contrast to traditional biometrics, ECG allows for continuous authentication and consequently expands the range of applications. However, time varying biometrics put on the line the recognition accuracy due to increased intra subject variability. This paper suggests a novel framework for bypassing this inadequacy. A template update methodology is proposed and demonstrated to boost the recognition performance over 2 hour recordings of 10 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":"133571910","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.2009.5339047
Kenta Takahashi, Shinji Hirata Hitachi
Biometric authentication has attracted attention because of its high security and convenience. However, biometric feature such as fingerprint can not be revoked like passwords. Thus once the biometric data of a user stored in the system has been compromised, it can not be used for authentication securely for his/her whole life long. To address this issue, an authentication scheme called cancelable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. In this paper, we propose new methods for generating cancelable fingerprint templates with provable security based on the well-known chip matching algorithm for fingerprint verification and correlation-invariant random filtering for transforming templates. Experimental evaluation shows that our methods can be applied to fingerprint authentication without much loss in accuracy compared with the conventional chip matching algorithm.
{"title":"Generating provably secure cancelable fingerprint templates based on correlation-invariant random filtering","authors":"Kenta Takahashi, Shinji Hirata Hitachi","doi":"10.1109/BTAS.2009.5339047","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339047","url":null,"abstract":"Biometric authentication has attracted attention because of its high security and convenience. However, biometric feature such as fingerprint can not be revoked like passwords. Thus once the biometric data of a user stored in the system has been compromised, it can not be used for authentication securely for his/her whole life long. To address this issue, an authentication scheme called cancelable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. In this paper, we propose new methods for generating cancelable fingerprint templates with provable security based on the well-known chip matching algorithm for fingerprint verification and correlation-invariant random filtering for transforming templates. Experimental evaluation shows that our methods can be applied to fingerprint authentication without much loss in accuracy compared with the conventional chip matching 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":"134629265","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}
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