Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics
Information fusion is a challenging problem in biometrics, where data comes from multiple biometric modalities or multiple feature spaces extracted from the same modality. Learning from heterogeneous data sources, in general, is termed as multi-view learning, where view is an encompassing term that...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9157880/ |
| _version_ | 1798002001887363072 |
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| author | Neeti Pokhriyal Venu Govindaraju |
| author_facet | Neeti Pokhriyal Venu Govindaraju |
| author_sort | Neeti Pokhriyal |
| collection | DOAJ |
| description | Information fusion is a challenging problem in biometrics, where data comes from multiple biometric modalities or multiple feature spaces extracted from the same modality. Learning from heterogeneous data sources, in general, is termed as multi-view learning, where view is an encompassing term that refers to different sets of observations having distinct statistical properties. Most of the existing approaches to learning from multiple views either assume that the views are either independent or fully dependent. However, in real scenarios, these assumptions are almost never truly satisfied. In this work, we relax these assumptions. We propose a feature fusion method called Discriminative Factorized Subspaces (DFS) that learns a factorized subspace consisting of a single shared subspace (that captures the common information), and view-specific subspaces that captures information specific to each view. DFS jointly learns these subspaces, by posing the optimization problem as a constrained Rayleigh Quotient based formulation, whose solution is efficiently obtained using generalized eigenvalue decomposition. Our method does not require lots of data to learn from, and we show how it is apt for domains characterized by limited training data, and high intra-class variability. As an application, we tackle the challenging problem of touchscreen biometrics, which is based on the study of user interactions with their touch screens. Through extensive experimentation and thorough evaluation, we demonstrate how DFS learns a better discriminatory boundary, and provides a superior performance than state of the art methods for touchscreen biometric verification. |
| first_indexed | 2024-04-11T11:45:14Z |
| format | Article |
| id | doaj.art-37104af874ef439eb24629365374c428 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:45:14Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-37104af874ef439eb24629365374c4282022-12-22T04:25:35ZengIEEEIEEE Access2169-35362020-01-01815250015251110.1109/ACCESS.2020.30141889157880Learning Discriminative Factorized Subspaces With Application to Touchscreen BiometricsNeeti Pokhriyal0https://orcid.org/0000-0001-9410-7084Venu Govindaraju1https://orcid.org/0000-0002-5318-7409Department of Computer Science, Dartmouth College, Hanover, NH, USADepartment of Computer Science, University at Buffalo (State University of New York at Buffalo), Buffalo, NY, USAInformation fusion is a challenging problem in biometrics, where data comes from multiple biometric modalities or multiple feature spaces extracted from the same modality. Learning from heterogeneous data sources, in general, is termed as multi-view learning, where view is an encompassing term that refers to different sets of observations having distinct statistical properties. Most of the existing approaches to learning from multiple views either assume that the views are either independent or fully dependent. However, in real scenarios, these assumptions are almost never truly satisfied. In this work, we relax these assumptions. We propose a feature fusion method called Discriminative Factorized Subspaces (DFS) that learns a factorized subspace consisting of a single shared subspace (that captures the common information), and view-specific subspaces that captures information specific to each view. DFS jointly learns these subspaces, by posing the optimization problem as a constrained Rayleigh Quotient based formulation, whose solution is efficiently obtained using generalized eigenvalue decomposition. Our method does not require lots of data to learn from, and we show how it is apt for domains characterized by limited training data, and high intra-class variability. As an application, we tackle the challenging problem of touchscreen biometrics, which is based on the study of user interactions with their touch screens. Through extensive experimentation and thorough evaluation, we demonstrate how DFS learns a better discriminatory boundary, and provides a superior performance than state of the art methods for touchscreen biometric verification.https://ieeexplore.ieee.org/document/9157880/Touchscreen biometricsmulti-modal biometricsmulti-modal datafeature fusion |
| spellingShingle | Neeti Pokhriyal Venu Govindaraju Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics IEEE Access Touchscreen biometrics multi-modal biometrics multi-modal data feature fusion |
| title | Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics |
| title_full | Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics |
| title_fullStr | Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics |
| title_full_unstemmed | Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics |
| title_short | Learning Discriminative Factorized Subspaces With Application to Touchscreen Biometrics |
| title_sort | learning discriminative factorized subspaces with application to touchscreen biometrics |
| topic | Touchscreen biometrics multi-modal biometrics multi-modal data feature fusion |
| url | https://ieeexplore.ieee.org/document/9157880/ |
| work_keys_str_mv | AT neetipokhriyal learningdiscriminativefactorizedsubspaceswithapplicationtotouchscreenbiometrics AT venugovindaraju learningdiscriminativefactorizedsubspaceswithapplicationtotouchscreenbiometrics |