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BiolD: a multimodal biometric identification system
Abstract
Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single feature, however, sometimes fails to be exact enough for identification. Another disadvantage of using only one feature is that the chosen feature is not always readable. Dialog Communication Systems (DCS AG) developed BioID, a multimodal identification system that uses three different features-face, voice, and lip movement-to identify people. With its three modalities, BioID achieves much greater accuracy than single-feature systems. Even if one modality is somehow disturbed-for example, if a noisy environment drowns out the voice-the ether two modalities still lead to an accurate identification. This article goes into detail about the system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data. The authors also explain the classification principles used for optical features and the sensor fusion options (the combinations of the three results-face, voice, lip movement-to obtain varying levels of security).
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Cites
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Cited by
An introduction to biometric recognition
Jain, A. K., Ross, A., & Prabhakar, S. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.818349 · Cited by 4,974
Multimodal Biometrics: An Overview
Ross, A., & Jain, A. (2004) · Zenodo · doi:10.5281/zenodo.38715 · Cited by 593
Academic Integrity in Online Assessment: A Research Review
Holden, O. L., Norris, M. E., & Kuhlmeier, V. A. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.639814 · Cited by 310
Deep multimodal fusion for semantic image segmentation: A survey
Zhang, Y., Sidibé, D., Morel, O., & Mériaudeau, F. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104042 · Cited by 261
Classification of User Movement Data
Arias, P., Kelley, C., Mason, J., Bryant, K., & Roy, K. (2018) · Proceedings of the 2nd International Conference on Digital Signal Processing · doi:10.1145/3193025.3193036 · Cited by 6
Metadata
- Authors
- Frischholz, R. W., & Dieckmann, U.
- Venue
- Computer
- Published
- 2000
- Volume
- 33
- Issue
- 2
- Pages
- 64-68
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2068723942
- Cited by
- 346
- References
- 1
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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