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Novel Technique for 3D Face Recognition Using Anthropometric Methodology

Novel Technique for 3D Face Recognition Using Anthropometric Methodology

Souhir Sghaier, Wajdi Farhat, Chokri Souani
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 18
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522543527|DOI: 10.4018/IJACI.2018010104
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MLA

Sghaier, Souhir, et al. "Novel Technique for 3D Face Recognition Using Anthropometric Methodology." IJACI vol.9, no.1 2018: pp.60-77. http://doi.org/10.4018/IJACI.2018010104

APA

Sghaier, S., Farhat, W., & Souani, C. (2018). Novel Technique for 3D Face Recognition Using Anthropometric Methodology. International Journal of Ambient Computing and Intelligence (IJACI), 9(1), 60-77. http://doi.org/10.4018/IJACI.2018010104

Chicago

Sghaier, Souhir, Wajdi Farhat, and Chokri Souani. "Novel Technique for 3D Face Recognition Using Anthropometric Methodology," International Journal of Ambient Computing and Intelligence (IJACI) 9, no.1: 60-77. http://doi.org/10.4018/IJACI.2018010104

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Abstract

This manuscript presents an improved system research that can detect and recognize the person in 3D space automatically and without the interaction of the people's faces. This system is based not only on a quantum computation and measurements to extract the vector features in the phase of characterization but also on learning algorithm (using SVM) to classify and recognize the person. This research presents an improved technique for automatic 3D face recognition using anthropometric proportions and measurement to detect and extract the area of interest which is unaffected by facial expression. This approach is able to treat incomplete and noisy images and reject the non-facial areas automatically. Moreover, it can deal with the presence of holes in the meshed and textured 3D image. It is also stable against small translation and rotation of the face. All the experimental tests have been done with two 3D face datasets FRAV 3D and GAVAB. Therefore, the test's results of the proposed approach are promising because they showed that it is competitive comparable to similar approaches in terms of accuracy, robustness, and flexibility. It achieves a high recognition performance rate of 95.35% for faces with neutral and non-neutral expressions for the identification and 98.36% for the authentification with GAVAB and 100% with some gallery of FRAV 3D datasets.

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