Multimodal 2D+3D face recognition

The growing interest in face recognition is fueled by its potential applications in commercial and law enforcement. Although humans seem to have an innate ability to recognize and distinguish between faces, computerized face recognition systems have not yet been able to achieve satisfactory performa...

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Main Author: I Gede Putra Kusuma Negara
Other Authors: Chua Chin Seng
Format: Theses and Dissertations
Language:English
Published: 2012
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Online Access:http://hdl.handle.net/10356/50899
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-508992023-07-04T16:39:20Z Multimodal 2D+3D face recognition I Gede Putra Kusuma Negara Chua Chin Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems The growing interest in face recognition is fueled by its potential applications in commercial and law enforcement. Although humans seem to have an innate ability to recognize and distinguish between faces, computerized face recognition systems have not yet been able to achieve satisfactory performances. The worldwide implementation of face recognition has been hindered by its inherent problems. The appearance of a face may vary significantly due to variations in illumination condition, head pose, and facial expression, to name a few. On the other hand, faces basically belong to the same class of objects. They consist of the same facial features in the same geometrical configuration. When all other factors are held constant, faces of different persons are somewhat similar and the differences between them can be quite subtle. Face recognition has been regarded as one of the fundamental problems in pattern analysis. In the past, research efforts have been focused on face recognition approaches based on 2D intensity images. However, their performances deteriorate under illumination and pose variations. There is evidence that 3D range images have the potential to overcome this problem. The explicit representation of a 3D shape is less sensitive to illumination and pose variations. However, the 3D data is vulnerable to surface deformation due to facial expression variations. Integrating 2D and 3D sensory information is believed to potentially improve the face recognition performance. Most of the proposed approaches of multimodal 2D+3D face recognition exploit the 2D and 3D information at the later stage. They do not fully benefit from the dependency between the 2D and 3D images. Furthermore, early fusion data contains richer information about the biometrics than the compressed features or matching scores. Therefore, I propose PCA- and FLD-based image recombination approaches to enrich the distinctive information contained in the images and to enhance the discriminating power of the recombined data. The PCA recombines the 2D and 3D images using decorrelation axes that account for the maximal amount of variances in the data. Meanwhile, the FLD calculates an optimum recombination transform that maximizes the ratio of the determinant of the between-class and within-class scatter matrices of the recombined data. Therefore, the recombined data is more discriminating for recognition than the original images. Doctor of Philosophy (EEE) 2012-12-17T03:33:50Z 2012-12-17T03:33:50Z 2012 2012 Thesis http://hdl.handle.net/10356/50899 en 167 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
I Gede Putra Kusuma Negara
Multimodal 2D+3D face recognition
description The growing interest in face recognition is fueled by its potential applications in commercial and law enforcement. Although humans seem to have an innate ability to recognize and distinguish between faces, computerized face recognition systems have not yet been able to achieve satisfactory performances. The worldwide implementation of face recognition has been hindered by its inherent problems. The appearance of a face may vary significantly due to variations in illumination condition, head pose, and facial expression, to name a few. On the other hand, faces basically belong to the same class of objects. They consist of the same facial features in the same geometrical configuration. When all other factors are held constant, faces of different persons are somewhat similar and the differences between them can be quite subtle. Face recognition has been regarded as one of the fundamental problems in pattern analysis. In the past, research efforts have been focused on face recognition approaches based on 2D intensity images. However, their performances deteriorate under illumination and pose variations. There is evidence that 3D range images have the potential to overcome this problem. The explicit representation of a 3D shape is less sensitive to illumination and pose variations. However, the 3D data is vulnerable to surface deformation due to facial expression variations. Integrating 2D and 3D sensory information is believed to potentially improve the face recognition performance. Most of the proposed approaches of multimodal 2D+3D face recognition exploit the 2D and 3D information at the later stage. They do not fully benefit from the dependency between the 2D and 3D images. Furthermore, early fusion data contains richer information about the biometrics than the compressed features or matching scores. Therefore, I propose PCA- and FLD-based image recombination approaches to enrich the distinctive information contained in the images and to enhance the discriminating power of the recombined data. The PCA recombines the 2D and 3D images using decorrelation axes that account for the maximal amount of variances in the data. Meanwhile, the FLD calculates an optimum recombination transform that maximizes the ratio of the determinant of the between-class and within-class scatter matrices of the recombined data. Therefore, the recombined data is more discriminating for recognition than the original images.
author2 Chua Chin Seng
author_facet Chua Chin Seng
I Gede Putra Kusuma Negara
format Theses and Dissertations
author I Gede Putra Kusuma Negara
author_sort I Gede Putra Kusuma Negara
title Multimodal 2D+3D face recognition
title_short Multimodal 2D+3D face recognition
title_full Multimodal 2D+3D face recognition
title_fullStr Multimodal 2D+3D face recognition
title_full_unstemmed Multimodal 2D+3D face recognition
title_sort multimodal 2d+3d face recognition
publishDate 2012
url http://hdl.handle.net/10356/50899
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