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Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model

Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model

Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri
Copyright: © 2015 |Volume: 4 |Issue: 2 |Pages: 20
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466680364|DOI: 10.4018/ijsda.2015040101
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MLA

Ganguly, Suranjan, et al. "Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model." IJSDA vol.4, no.2 2015: pp.1-20. http://doi.org/10.4018/ijsda.2015040101

APA

Ganguly, S., Bhattacharjee, D., & Nasipuri, M. (2015). Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model. International Journal of System Dynamics Applications (IJSDA), 4(2), 1-20. http://doi.org/10.4018/ijsda.2015040101

Chicago

Ganguly, Suranjan, Debotosh Bhattacharjee, and Mita Nasipuri. "Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 1-20. http://doi.org/10.4018/ijsda.2015040101

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Abstract

In this paper the pivotal contribution of the authors is to recognize the 3D face images from range images in the unconstrained environment i.e. under varying illumination, pose as well as occlusion that are considered to be the most challenging task in the domain of face recognition. During this investigation, face images have been normalized in terms of pose registration as well as occlusion restoration using ERFI (Energy Range Face Image) model. 3D face images are inherently illumination invariant due its point-based representation of data along three axes. Here, other than quantitative analysis, a subjective analysis is also carried out. However, synthesized datasets have been accomplished to investigate the performance of recognition rate from Frav3D and Bosphorus databases using SIFT and SURF like features. Moreover, weighted fusion of these individual feature sets is also done. Later these feature sets have been classified by K-NN and Sequence Matching Technique and achieved maximum recognition rates of 99.17% and 98.81% for Frav3D and GavabDB databases respectively.

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