Abstract
The presence of significant levels of signal variability in face-portrait type of images, due to differences in illumination, pose and expression, is generally been accepted as having an adverse effect on the overall performance of i) face modeling and synthesis (FM/S) and also on ii) face recognition (FR) systems. Furthermore, the dependency on such input data variability and thus the sensitivity, with respect to face synthesis performance, of Active Appearance Modeling (AAM), is also well understood. As a result, the Multi-Model Active Appearance Model (MM-AAM) technique [1] has been developed and shown to possess a superior face synthesis performance than AAM. This paper considers the applicability in FR applications of both AAM and MM-AAM face modeling and synthesis approaches. Thus, a MM-AAM methodology has been devised that is tailored to operate successfully within the context of face recognition. Experimental results show FR-MM-AAM to be significantly superior to conventional FR-AAM. © 2014 EURASIP.
Original language | English |
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Title of host publication | 22nd European Signal Processing Conference, EUSIPCO 2014 |
Publisher | IEEE |
Pages | 2445-2449 |
Number of pages | 5 |
ISBN (Print) | 9780992862619 |
Publication status | Published - 10 Nov 2014 |
Event | 22nd European Signal Processing Conference - Lisbon, Portugal Duration: 1 Sept 2014 → 5 Sept 2014 Conference number: 109162 |
Publication series
Name | European Signal Processing Conference |
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Publisher | IEEE |
ISSN (Print) | 2219-5491 |
Conference
Conference | 22nd European Signal Processing Conference |
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Abbreviated title | EUSIPCO 2014 |
Country/Territory | Portugal |
City | Lisbon |
Period | 1/09/14 → 5/09/14 |