Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117532
Yasushi Makihara, D. Muramatsu, Y. Yagi, Md. Altab Hossain
This paper describes a method of score-level fusion to optimize a Receiver Operating Characteristic (ROC) curve for multimodal biometrics. When the Probability Density Functions (PDFs) of the multimodal scores for each client and imposter are obtained from the training samples, it is well known that the isolines of a function of probabilistic densities, such as the likelihood ratio, posterior, or Bayes error gradient, give the optimal ROC curve. The success of the probability density-based methods depends on the PDF estimation for each client and imposter, which still remains a challenging problem. Therefore, we introduce a framework of direct estimation of the Bayes error gradient that bypasses the troublesome PDF estimation for each client and imposter. The lattice-type control points are allocated in a multiple score space, and the Bayes error gradients on the control points are then estimated in a comprehensive manner in the energy minimization framework including not only the data fitness of the training samples but also the boundary conditions and monotonic increase constraints to suppress the over-training. The experimental results for both simulation and real public data show the effectiveness of the proposed method.
{"title":"Score-level fusion based on the direct estimation of the Bayes error gradient distribution","authors":"Yasushi Makihara, D. Muramatsu, Y. Yagi, Md. Altab Hossain","doi":"10.1109/IJCB.2011.6117532","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117532","url":null,"abstract":"This paper describes a method of score-level fusion to optimize a Receiver Operating Characteristic (ROC) curve for multimodal biometrics. When the Probability Density Functions (PDFs) of the multimodal scores for each client and imposter are obtained from the training samples, it is well known that the isolines of a function of probabilistic densities, such as the likelihood ratio, posterior, or Bayes error gradient, give the optimal ROC curve. The success of the probability density-based methods depends on the PDF estimation for each client and imposter, which still remains a challenging problem. Therefore, we introduce a framework of direct estimation of the Bayes error gradient that bypasses the troublesome PDF estimation for each client and imposter. The lattice-type control points are allocated in a multiple score space, and the Bayes error gradients on the control points are then estimated in a comprehensive manner in the energy minimization framework including not only the data fitness of the training samples but also the boundary conditions and monotonic increase constraints to suppress the over-training. The experimental results for both simulation and real public data show the effectiveness of the proposed method.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128378777","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117596
G. Santos, Hugo Proença
Corner detection has motivated a great deal of research and is particularly important in a variety of tasks related to computer vision, acting as a basis for further stages. In particular, the detection of eye-corners in facial images is important in applications in biometric systems and assisted-driving systems. We empirically evaluated the state-of-the-art of eye-corner detection proposals and found that they achieve satisfactory results only when dealing with high-quality data. Hence, in this paper, we describe an eye-corner detection method that emphasizes robustness, i.e., its ability to deal with degraded data, and applicability to real-world conditions. Our experiments show that the proposed method outperforms others in both noise-free and degraded data (blurred and rotated images and images with significant variations in scale), which is a major achievement.
{"title":"A robust eye-corner detection method for real-world data","authors":"G. Santos, Hugo Proença","doi":"10.1109/IJCB.2011.6117596","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117596","url":null,"abstract":"Corner detection has motivated a great deal of research and is particularly important in a variety of tasks related to computer vision, acting as a basis for further stages. In particular, the detection of eye-corners in facial images is important in applications in biometric systems and assisted-driving systems. We empirically evaluated the state-of-the-art of eye-corner detection proposals and found that they achieve satisfactory results only when dealing with high-quality data. Hence, in this paper, we describe an eye-corner detection method that emphasizes robustness, i.e., its ability to deal with degraded data, and applicability to real-world conditions. Our experiments show that the proposed method outperforms others in both noise-free and degraded data (blurred and rotated images and images with significant variations in scale), which is a major achievement.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132879271","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117553
K. Inthavisas, D. Lopresti
In this paper, we propose a way to combine a password with a speech biometric cryptosystem. We present two schemes to enhance verification performance in a biometric cryptosystem using password. Both can resist a password brute-force search if biometrics are not compromised. Even if the biometrics are compromised, attackers have to spend many more attempts in searching for cryptographic keys when we compare ours with a traditional password-based approach. In addition, the experimental results show that the verification performance is significantly improved.
{"title":"Speech cryptographic key regeneration based on password","authors":"K. Inthavisas, D. Lopresti","doi":"10.1109/IJCB.2011.6117553","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117553","url":null,"abstract":"In this paper, we propose a way to combine a password with a speech biometric cryptosystem. We present two schemes to enhance verification performance in a biometric cryptosystem using password. Both can resist a password brute-force search if biometrics are not compromised. Even if the biometrics are compromised, attackers have to spend many more attempts in searching for cryptographic keys when we compare ours with a traditional password-based approach. In addition, the experimental results show that the verification performance is significantly improved.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128555340","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117521
Omar Ocegueda, G. Passalis, T. Theoharis, S. Shah, I. Kakadiaris
We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.
{"title":"UR3D-C: Linear dimensionality reduction for efficient 3D face recognition","authors":"Omar Ocegueda, G. Passalis, T. Theoharis, S. Shah, I. Kakadiaris","doi":"10.1109/IJCB.2011.6117521","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117521","url":null,"abstract":"We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811155","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117485
Yilin Li, Baochang Zhang, Yao Cao, Sanqiang Zhao, Yongsheng Gao, Jianzhuang Liu
This paper introduces a new BeiHang (BH) Keystroke Dynamics Database for testing and evaluation of biometric approaches. Different from the existing keystroke dynamics researches which solely rely on laboratory experiments, the developed database is collected from a real commercialized system and thus is more comprehensive and more faithful to human behavior. Moreover, our database comes with ready-to-use benchmark results of three keystroke dynamics methods, Nearest Neighbor classifier, Gaussian Model and One-Class Support Vector Machine. Both the database and benchmark results are open to the public and provide a significant experimental platform for international researchers in the keystroke dynamics area.
{"title":"Study on the BeiHang Keystroke Dynamics Database","authors":"Yilin Li, Baochang Zhang, Yao Cao, Sanqiang Zhao, Yongsheng Gao, Jianzhuang Liu","doi":"10.1109/IJCB.2011.6117485","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117485","url":null,"abstract":"This paper introduces a new BeiHang (BH) Keystroke Dynamics Database for testing and evaluation of biometric approaches. Different from the existing keystroke dynamics researches which solely rely on laboratory experiments, the developed database is collected from a real commercialized system and thus is more comprehensive and more faithful to human behavior. Moreover, our database comes with ready-to-use benchmark results of three keystroke dynamics methods, Nearest Neighbor classifier, Gaussian Model and One-Class Support Vector Machine. Both the database and benchmark results are open to the public and provide a significant experimental platform for international researchers in the keystroke dynamics area.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125020835","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117523
R. Kumar, B. Bhanu, Subir Ghosh, N. Thakoor
The performance of a recognition system is usually experimentally determined. Therefore, one cannot predict the performance of a recognition system a priori for a new dataset. In this paper, a statistical model to predict the value of k in the rank-k identification rate for a given biometric system is presented. Thus, one needs to search only the topmost k match scores to locate the true match object. A geometrical probability distribution is used to model the number of non match scores present in the set of similarity scores. The model is tested in simulation and by using a public dataset. The model is also indirectly validated against the previously published results. The actual results obtained using publicly available database are very close to the predicted results which validates the proposed model.
{"title":"Prediction and validation of indexing performance for biometrics","authors":"R. Kumar, B. Bhanu, Subir Ghosh, N. Thakoor","doi":"10.1109/IJCB.2011.6117523","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117523","url":null,"abstract":"The performance of a recognition system is usually experimentally determined. Therefore, one cannot predict the performance of a recognition system a priori for a new dataset. In this paper, a statistical model to predict the value of k in the rank-k identification rate for a given biometric system is presented. Thus, one needs to search only the topmost k match scores to locate the true match object. A geometrical probability distribution is used to model the number of non match scores present in the set of similarity scores. The model is tested in simulation and by using a public dataset. The model is also indirectly validated against the previously published results. The actual results obtained using publicly available database are very close to the predicted results which validates the proposed model.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127810236","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117482
Soweon Yoon, Jianjiang Feng, Anil K. Jain
Latent fingerprints, or simply latents, have been considered as cardinal evidence for identifying and convicting criminals. The amount of information available for identification from latents is often limited due to their poor quality, unclear ridge structure and occlusion with complex background or even other latent prints. We propose a latent fingerprint enhancement algorithm, which expects manually marked region of interest (ROI) and singular points. The core of the proposed algorithm is a robust orientation field estimation algorithm for latents. Short-time Fourier transform is used to obtain multiple orientation elements in each image block. This is followed by a hypothesize-and-test paradigm based on randomized RANSAC, which generates a set of hypothesized orientation fields. Experimental results on NIST SD27 latent fingerprint database show that the matching performance of a commercial matcher is significantly improved by utilizing the enhanced latent fingerprints produced by the proposed algorithm.
{"title":"Latent fingerprint enhancement via robust orientation field estimation","authors":"Soweon Yoon, Jianjiang Feng, Anil K. Jain","doi":"10.1109/IJCB.2011.6117482","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117482","url":null,"abstract":"Latent fingerprints, or simply latents, have been considered as cardinal evidence for identifying and convicting criminals. The amount of information available for identification from latents is often limited due to their poor quality, unclear ridge structure and occlusion with complex background or even other latent prints. We propose a latent fingerprint enhancement algorithm, which expects manually marked region of interest (ROI) and singular points. The core of the proposed algorithm is a robust orientation field estimation algorithm for latents. Short-time Fourier transform is used to obtain multiple orientation elements in each image block. This is followed by a hypothesize-and-test paradigm based on randomized RANSAC, which generates a set of hypothesized orientation fields. Experimental results on NIST SD27 latent fingerprint database show that the matching performance of a commercial matcher is significantly improved by utilizing the enhanced latent fingerprints produced by the proposed algorithm.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123016673","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117500
Vishnu Naresh Boddeti, J. Smereka, B. Kumar
Iris recognition is believed to offer excellent recognition rates for iris images acquired under controlled conditions. However, recognition rates degrade considerably when images exhibit impairments such as off-axis gaze, partial occlusions, specular reflections and out-of-focus and motion-induced blur. In this paper, we use the recently-available face and ocular challenge set (FOCS) to investigate the comparative recognition performance gains of using ocular images (i.e., iris regions as well as the surrounding peri-ocular regions) instead of just the iris regions. A new method for ocular recognition is presented and it is shown that use of ocular regions leads to better recognition rates than iris recognition on FOCS dataset. Another advantage of using ocular images for recognition is that it avoids the need for segmenting the iris images from their surrounding regions.
{"title":"A comparative evaluation of iris and ocular recognition methods on challenging ocular images","authors":"Vishnu Naresh Boddeti, J. Smereka, B. Kumar","doi":"10.1109/IJCB.2011.6117500","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117500","url":null,"abstract":"Iris recognition is believed to offer excellent recognition rates for iris images acquired under controlled conditions. However, recognition rates degrade considerably when images exhibit impairments such as off-axis gaze, partial occlusions, specular reflections and out-of-focus and motion-induced blur. In this paper, we use the recently-available face and ocular challenge set (FOCS) to investigate the comparative recognition performance gains of using ocular images (i.e., iris regions as well as the surrounding peri-ocular regions) instead of just the iris regions. A new method for ocular recognition is presented and it is shown that use of ocular regions leads to better recognition rates than iris recognition on FOCS dataset. Another advantage of using ocular images for recognition is that it avoids the need for segmenting the iris images from their surrounding regions.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115167951","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117533
Devansh Arpit, A. Namboodiri
This paper deals with extraction of fingerprint features directly from gray scale images by the method of ridge tracing. While doing so, we make substantial use of contextual information gathered during the tracing process. Narrow bandpass based filtering methods for fingerprint image enhancement are extremely robust as noisy regions do not affect the result of cleaner ones. However, these method often generate artifacts whenever the underlying image does not fit the filter model, which may be due to the presence of noise and singularities. The proposed method allows us to use the contextual information to better handle such noisy regions. Moreover, the various parameters used in the algorithm have been made adaptive in order to circumvent human supervision. The experimental results from our algorithm have been compared with those from Gabor based filtering and feature extraction, as well as with the original ridge tracing work from Maio and Maltoni [11]. The results clearly indicate that the proposed approach makes ridge tracing more robust to noise and makes the extracted features more reliable.
{"title":"Fingerprint feature extraction from gray scale images by ridge tracing","authors":"Devansh Arpit, A. Namboodiri","doi":"10.1109/IJCB.2011.6117533","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117533","url":null,"abstract":"This paper deals with extraction of fingerprint features directly from gray scale images by the method of ridge tracing. While doing so, we make substantial use of contextual information gathered during the tracing process. Narrow bandpass based filtering methods for fingerprint image enhancement are extremely robust as noisy regions do not affect the result of cleaner ones. However, these method often generate artifacts whenever the underlying image does not fit the filter model, which may be due to the presence of noise and singularities. The proposed method allows us to use the contextual information to better handle such noisy regions. Moreover, the various parameters used in the algorithm have been made adaptive in order to circumvent human supervision. The experimental results from our algorithm have been compared with those from Gabor based filtering and feature extraction, as well as with the original ridge tracing work from Maio and Maltoni [11]. The results clearly indicate that the proposed approach makes ridge tracing more robust to noise and makes the extracted features more reliable.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519476","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 : 2011-10-11DOI: 10.1109/IJCB.2011.6117600
Felix Juefei-Xu, Khoa Luu, M. Savvides, T. D. Bui, C. Suen
In this paper, we will present a novel framework of utilizing periocular region for age invariant face recognition. To obtain age invariant features, we first perform preprocessing schemes, such as pose correction, illumination and periocular region normalization. And then we apply robust Walsh-Hadamard transform encoded local binary patterns (WLBP) on preprocessed periocular region only. We find the WLBP feature on periocular region maintains consistency of the same individual across ages. Finally, we use unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database. Compared to published results, our proposed approach yields the best recognition and identification results.
{"title":"Investigating age invariant face recognition based on periocular biometrics","authors":"Felix Juefei-Xu, Khoa Luu, M. Savvides, T. D. Bui, C. Suen","doi":"10.1109/IJCB.2011.6117600","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117600","url":null,"abstract":"In this paper, we will present a novel framework of utilizing periocular region for age invariant face recognition. To obtain age invariant features, we first perform preprocessing schemes, such as pose correction, illumination and periocular region normalization. And then we apply robust Walsh-Hadamard transform encoded local binary patterns (WLBP) on preprocessed periocular region only. We find the WLBP feature on periocular region maintains consistency of the same individual across ages. Finally, we use unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database. Compared to published results, our proposed approach yields the best recognition and identification results.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121954701","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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