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Automated biometrics of audio-visual multiple modals
| Title: | Automated biometrics of audio-visual multiple modals. |
 155 views 61 downloads |
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| Name(s): |
Huang, Lin College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Issued: | 2010 | |
| Publisher: | Florida Atlantic University | |
| Physical Form: | electronic | |
| Extent: | xiv, 162 p. : ill. (some col.) | |
| Language(s): | English | |
| Summary: | Biometrics is the science and technology of measuring and analyzing biological data for authentication purposes. Its progress has brought in a large number of civilian and government applications. The candidate modalities used in biometrics include retinas, fingerprints, signatures, audio, faces, etc. There are two types of biometric system: single modal systems and multiple modal systems. Single modal systems perform person recognition based on a single biometric modality and are affected by problems like noisy sensor data, intra-class variations, distinctiveness and non-universality. Applying multiple modal systems that consolidate evidence from multiple biometric modalities can alleviate those problems of single modal ones. Integration of evidence obtained from multiple cues, also known as fusion, is a critical part in multiple modal systems, and it may be consolidated at several levels like feature fusion level, matching score fusion level and decision fusion level. Among biometric modalities, both audio and face modalities are easy to use and generally acceptable by users. Furthermore, the increasing availability and the low cost of audio and visual instruments make it feasible to apply such Audio-Visual (AV) systems for security applications. Therefore, this dissertation proposes an algorithm of face recognition. In addition, it has developed some novel algorithms of fusion in different levels for multiple modal biometrics, which have been tested by a virtual database and proved to be more reliable and robust than systems that rely on a single modality. | |
| Identifier: | 612342773 (oclc), 1927864 (digitool), FADT1927864 (IID), fau:2966 (fedora) | |
| Note(s): |
by Lin Huang. Thesis (Ph.D.)--Florida Atlantic University, 2010. Includes bibliography. Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web. |
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| Subject(s): |
Pattern recognition systems Optical pattern recognition Biometric identification Identification -- Automation Automatic speech recognition |
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| Persistent Link to This Record: | http://purl.flvc.org/FAU/1927864 | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU |
