Pub Date : 2009-09-28DOI: 10.1109/BTAS.2009.5339021
Ajay Kumar, Yingbo Zhou
The usage of finger knuckle images for personal identification has shown promising results and generated lot of interest in biometrics. In this work, we investigate a new approach for efficient and effective personal identification using KnuckleCodes. The enhanced knuckle images are employed to generate KnuckleCodes using localized Radon transform that can efficiently characterize random curved lines and creases. The similarity between two KnuckleCodes is computed from the minimum matching distance that can account for the variations resulting from translation and positioning of fingers. The feasibility of the proposed approach is investigated on the finger knuckle database from 158 subjects. The experimental results, i.e., equal error rate of 1.08% and rank one recognition rate of 98.6%, suggest the utility of the proposed approach for online human identification.
{"title":"Human identification using KnuckleCodes","authors":"Ajay Kumar, Yingbo Zhou","doi":"10.1109/BTAS.2009.5339021","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339021","url":null,"abstract":"The usage of finger knuckle images for personal identification has shown promising results and generated lot of interest in biometrics. In this work, we investigate a new approach for efficient and effective personal identification using KnuckleCodes. The enhanced knuckle images are employed to generate KnuckleCodes using localized Radon transform that can efficiently characterize random curved lines and creases. The similarity between two KnuckleCodes is computed from the minimum matching distance that can account for the variations resulting from translation and positioning of fingers. The feasibility of the proposed approach is investigated on the finger knuckle database from 158 subjects. The experimental results, i.e., equal error rate of 1.08% and rank one recognition rate of 98.6%, suggest the utility of the proposed approach for online human identification.","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":"117312117","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.2009.5339026
Y. Lui, J. Beveridge, M. Kirby
This paper presents a new paradigm for face recognition in illumination spaces when the identities of training subjects and test subjects do not overlap. Previous methods employ illumination models to create a projector from an illumination basis and perform single image classification. In contrast, we apply an illumination model to an image and create a set of illumination variants. For a gallery image, these variants are expressed as a point on a Stiefel manifold with an associated tangent plane. Two projections of the probe image illumination variants onto this tangent plane are defined and the ratio between these two projections, called the Canonical Stiefel Quotient (CSQ), is a measure of distance between images. We show that the proposed CSQ paradigm not only outperforms the traditional single image matching approach but also other variants of image set matching including a geodesic method. Furthermore, the proposed CSQ method is robust to the choice of training sets. Finally, our analyses reveal the benefits of using image set classification over single image matching.
{"title":"Canonical Stiefel Quotient and its application to generic face recognition in illumination spaces","authors":"Y. Lui, J. Beveridge, M. Kirby","doi":"10.1109/BTAS.2009.5339026","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339026","url":null,"abstract":"This paper presents a new paradigm for face recognition in illumination spaces when the identities of training subjects and test subjects do not overlap. Previous methods employ illumination models to create a projector from an illumination basis and perform single image classification. In contrast, we apply an illumination model to an image and create a set of illumination variants. For a gallery image, these variants are expressed as a point on a Stiefel manifold with an associated tangent plane. Two projections of the probe image illumination variants onto this tangent plane are defined and the ratio between these two projections, called the Canonical Stiefel Quotient (CSQ), is a measure of distance between images. We show that the proposed CSQ paradigm not only outperforms the traditional single image matching approach but also other variants of image set matching including a geodesic method. Furthermore, the proposed CSQ method is robust to the choice of training sets. Finally, our analyses reveal the benefits of using image set classification over single image 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":"123228884","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.2009.5339080
R. N. Rodrigues, Greyce N. Schroeder, Jason J. Corso, V. Govindaraju
In this paper, we explore new methods to improve the modeling of facial images under different types of variations like pose, ambient illumination and facial expression. We investigate the intuitive assumption that the parameters for the distribution of facial images change smoothly with respect to variations in the face pose angle. A Markov Random Field is defined to model a smooth prior over the parameter space and the maximum a posteriori solution is computed. We also propose extensions to the view-based face recognition method by learning how to traverse between different subspaces so we can synthesize facial images with different characteristics for the same person. This allow us to enroll a new user with a single 2D image.
{"title":"Unconstrained face recognition using MRF priors and manifold traversing","authors":"R. N. Rodrigues, Greyce N. Schroeder, Jason J. Corso, V. Govindaraju","doi":"10.1109/BTAS.2009.5339080","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339080","url":null,"abstract":"In this paper, we explore new methods to improve the modeling of facial images under different types of variations like pose, ambient illumination and facial expression. We investigate the intuitive assumption that the parameters for the distribution of facial images change smoothly with respect to variations in the face pose angle. A Markov Random Field is defined to model a smooth prior over the parameter space and the maximum a posteriori solution is computed. We also propose extensions to the view-based face recognition method by learning how to traverse between different subspaces so we can synthesize facial images with different characteristics for the same person. This allow us to enroll a new user with a single 2D image.","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":"127539835","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.2009.5339040
W. Scheirer, A. Rocha, B. Heflin, T. Boult
Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A new machine learning approach and several correlation filter approaches, including a new adaptive variant, are compared. We present experimental results on a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system. The results of our experiments show that our new detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem.
{"title":"Difficult detection: A comparison of two different approaches to eye detection for unconstrained environments","authors":"W. Scheirer, A. Rocha, B. Heflin, T. Boult","doi":"10.1109/BTAS.2009.5339040","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339040","url":null,"abstract":"Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A new machine learning approach and several correlation filter approaches, including a new adaptive variant, are compared. We present experimental results on a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system. The results of our experiments show that our new detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem.","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":"116579819","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.2009.5339014
Hosik Sohn, Yong Man Ro, K. Plataniotis
In this paper, we propose a revocable and privacy preserving template of face biometrics based on random projection. The face biometric is augmented and simultaneously projected onto random subspace. The face image vector is augmented by adding the vector whose elements are varying with zero mean. The augmented face vector can provide better accuracy of authentication and privacy preservation. We analyze the similarity, privacy preserving and security properties of the proposed augmented face biometric information in the random projection-domain. To demonstrate the feasibility of the proposed method, detailed theoretical analysis and several experimental results are provided. The results show that our method is able to provide revocability and privacy preservation while offering better authentication accuracy and security.
{"title":"Biometric authentication using augmented face and random projection","authors":"Hosik Sohn, Yong Man Ro, K. Plataniotis","doi":"10.1109/BTAS.2009.5339014","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339014","url":null,"abstract":"In this paper, we propose a revocable and privacy preserving template of face biometrics based on random projection. The face biometric is augmented and simultaneously projected onto random subspace. The face image vector is augmented by adding the vector whose elements are varying with zero mean. The augmented face vector can provide better accuracy of authentication and privacy preservation. We analyze the similarity, privacy preserving and security properties of the proposed augmented face biometric information in the random projection-domain. To demonstrate the feasibility of the proposed method, detailed theoretical analysis and several experimental results are provided. The results show that our method is able to provide revocability and privacy preservation while offering better authentication accuracy and security.","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":"126063406","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.2009.5339024
Yu Chen, M. Adjouadi, A. Barreto, N. Rishe, J. Andrian
This research introduces a noise-resistant and computational efficient segmentation approach towards less constrained iris recognition. The UBIRIS.v2 database which contains close-up eye images taken under visible light is used to test the proposed algorithm. The proposed segmentation approach is based on a modified and fast Hough transform augmented with a newly developed strategy to define iris boundaries with multi-arcs and multi-lines. This optimized iris segmentation approach achieves excellent results in both accuracy (2% error) and execution speed (≤0.5s / image) using a 2.4GHz Intel® Q6600 processor with 2GB of RAM. This 2% error is an Exclusive-OR function in term of disagreeing pixels between the correct iris considered by the NICE.I committee and the segmented results from the proposed approach. The segmentation performance was independently evaluated in the “Noisy Iris Challenge Evaluation”, involving 97 participants worldwide, and ranking this research group in the top 6.
{"title":"A computational efficient iris extraction approach in unconstrained environments","authors":"Yu Chen, M. Adjouadi, A. Barreto, N. Rishe, J. Andrian","doi":"10.1109/BTAS.2009.5339024","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339024","url":null,"abstract":"This research introduces a noise-resistant and computational efficient segmentation approach towards less constrained iris recognition. The UBIRIS.v2 database which contains close-up eye images taken under visible light is used to test the proposed algorithm. The proposed segmentation approach is based on a modified and fast Hough transform augmented with a newly developed strategy to define iris boundaries with multi-arcs and multi-lines. This optimized iris segmentation approach achieves excellent results in both accuracy (2% error) and execution speed (≤0.5s / image) using a 2.4GHz Intel® Q6600 processor with 2GB of RAM. This 2% error is an Exclusive-OR function in term of disagreeing pixels between the correct iris considered by the NICE.I committee and the segmented results from the proposed approach. The segmentation performance was independently evaluated in the “Noisy Iris Challenge Evaluation”, involving 97 participants worldwide, and ranking this research group in the top 6.","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":"128167304","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.2009.5339061
Ron F. Stewart, Matt Estevao, A. Adler
This paper reports on tests of the performance of fingerprint recognition technology in rugged outdoor conditions, with an especial concentration on the performance in cold weather. We analyze: 1) chip versus optical fingerprint scanner technology, 2) recognition performance and image quality, and 3) user/device interaction. A outdoor fingerprint door access system was designed to capture fingerprint images and video data of user interactions. Using this device, data were captured over a period of two years, and a user survey performed. Data were analyzed in terms of biometric error rates and fingerprint quality (NFIQ) as a function of temperature and humidity. Results suggest: 1) biometric performance has no significant dependence on temperature and humidity (-30C to +20C), 2) both chip based and optical fingerprint scanners have some flaws in rugged and cold weather applications, and 3) overall fingerprint biometric technology has a good level of usability in this application.
{"title":"Fingerprint recognition performance in rugged outdoors and cold weather conditions","authors":"Ron F. Stewart, Matt Estevao, A. Adler","doi":"10.1109/BTAS.2009.5339061","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339061","url":null,"abstract":"This paper reports on tests of the performance of fingerprint recognition technology in rugged outdoor conditions, with an especial concentration on the performance in cold weather. We analyze: 1) chip versus optical fingerprint scanner technology, 2) recognition performance and image quality, and 3) user/device interaction. A outdoor fingerprint door access system was designed to capture fingerprint images and video data of user interactions. Using this device, data were captured over a period of two years, and a user survey performed. Data were analyzed in terms of biometric error rates and fingerprint quality (NFIQ) as a function of temperature and humidity. Results suggest: 1) biometric performance has no significant dependence on temperature and humidity (-30C to +20C), 2) both chip based and optical fingerprint scanners have some flaws in rugged and cold weather applications, and 3) overall fingerprint biometric technology has a good level of usability in this application.","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":"115070535","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.2009.5339009
M. Romero, Nick E. Pears
Our pose-invariant point-pair descriptors, which encode 3D shape between a pair of 3D points are described and evaluated. Two variants of descriptor are introduced, the first is the point-pair spin image, which is related to the classical spin image of Johnson and Hebert, and the second is derived from an implicit radial basis function (RBF) model of the facial surface. We call this a cylindrically sampled RBF (CSR) shape histogram. These descriptors can effectively encode edges in graph based representations of 3D shapes. Thus, they are useful in a wide range of 3D graph-based retrieval applications. Here we show how the descriptors are able to identify the nose-tip and the eye-corner of a human face simultaneously in six promising landmark localisation systems. We evaluate our approaches by computing root mean square errors of estimated landmark locations against our ground truth landmark localisations within the 3D Face Recognition Grand Challenge database.
{"title":"Point-pair descriptors for 3D facial landmark localisation","authors":"M. Romero, Nick E. Pears","doi":"10.1109/BTAS.2009.5339009","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339009","url":null,"abstract":"Our pose-invariant point-pair descriptors, which encode 3D shape between a pair of 3D points are described and evaluated. Two variants of descriptor are introduced, the first is the point-pair spin image, which is related to the classical spin image of Johnson and Hebert, and the second is derived from an implicit radial basis function (RBF) model of the facial surface. We call this a cylindrically sampled RBF (CSR) shape histogram. These descriptors can effectively encode edges in graph based representations of 3D shapes. Thus, they are useful in a wide range of 3D graph-based retrieval applications. Here we show how the descriptors are able to identify the nose-tip and the eye-corner of a human face simultaneously in six promising landmark localisation systems. We evaluate our approaches by computing root mean square errors of estimated landmark locations against our ground truth landmark localisations within the 3D Face Recognition Grand Challenge 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":"115192114","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.2009.5339045
E. Kelkboom, X. Zhou, J. Breebaart, R. Veldhuis, C. Busch
The popularity of biometrics and its widespread use introduces privacy risks. To mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometrics were introduced, also known as the field of template protection. In parallel to these developments, fusion of multiple sources of biometric information have shown to improve the verification performance of the biometric system. In this work we analyze fusion of the protected template from two 3D recognition algorithms (multi-algorithm fusion) at feature-, score-, and decision-level. We show that fusion can be applied at the known fusion-levels with the template protection technique known as the Helper-Data System. We also illustrate the required changes of the Helper-Data System and its corresponding limitations. Furthermore, our experimental results, based on 3D face range images of the FRGC v2 dataset, show that indeed fusion improves the verification performance.
{"title":"Multi-algorithm fusion with template protection","authors":"E. Kelkboom, X. Zhou, J. Breebaart, R. Veldhuis, C. Busch","doi":"10.1109/BTAS.2009.5339045","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339045","url":null,"abstract":"The popularity of biometrics and its widespread use introduces privacy risks. To mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometrics were introduced, also known as the field of template protection. In parallel to these developments, fusion of multiple sources of biometric information have shown to improve the verification performance of the biometric system. In this work we analyze fusion of the protected template from two 3D recognition algorithms (multi-algorithm fusion) at feature-, score-, and decision-level. We show that fusion can be applied at the known fusion-levels with the template protection technique known as the Helper-Data System. We also illustrate the required changes of the Helper-Data System and its corresponding limitations. Furthermore, our experimental results, based on 3D face range images of the FRGC v2 dataset, show that indeed fusion improves the verification performance.","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":"114141102","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.2009.5339078
B. Efraty, E. Ismailov, S. Shah, I. Kakadiaris
In this paper, we present a fully automatic system for face recognition based on a silhouette of the face profile. Previous research has demonstrated the high discriminative potential of this biometric. However, for the successful employment of this characteristic one is confronted with many challenges, such as the sensitivity of a profile's geometry to face rotation and the difficulty of accurate profile extraction from images. We propose to explore the feature space of profiles under various rotations with the aid of a 3D face model. In the enrollment mode, 3D data of subjects are acquired and used to create profiles under different rotations. The features extracted from these profiles are used to train a classifier. In the identification mode, the profiles are extracted from side view images using a modified Active Shape Model approach. We validate the accuracy of the extractor and the robustness of classification algorithms using data from a publicly available database.
{"title":"Towards 3D-aided profile-based face recognition","authors":"B. Efraty, E. Ismailov, S. Shah, I. Kakadiaris","doi":"10.1109/BTAS.2009.5339078","DOIUrl":"https://doi.org/10.1109/BTAS.2009.5339078","url":null,"abstract":"In this paper, we present a fully automatic system for face recognition based on a silhouette of the face profile. Previous research has demonstrated the high discriminative potential of this biometric. However, for the successful employment of this characteristic one is confronted with many challenges, such as the sensitivity of a profile's geometry to face rotation and the difficulty of accurate profile extraction from images. We propose to explore the feature space of profiles under various rotations with the aid of a 3D face model. In the enrollment mode, 3D data of subjects are acquired and used to create profiles under different rotations. The features extracted from these profiles are used to train a classifier. In the identification mode, the profiles are extracted from side view images using a modified Active Shape Model approach. We validate the accuracy of the extractor and the robustness of classification algorithms using data from a publicly available 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":"130661063","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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