Novel Technique for 3D Face Recognition Using Anthropometric Methodology

Novel Technique for 3D Face Recognition Using Anthropometric Methodology

Souhir Sghaier (Faculty of Sciences, Monastir University, Monastir, Tunisia), Wajdi Farhat (National School of Engineers, Sousse University, Sousse, Tunisia) and Chokri Souani (Higher Institute of Applied Sciences and Technology, Sousse University, Sousse, Tunisia)
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJACI.2018010104
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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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An extensive number of surveys for 3D face recognition system already exist. This section presents a brief overview of some works that are interested in such system.

The research work of Salazar et al. (2014) used the method of Markov network to characterize the 3D face. Their experimental results have shown a rate of 94.9% of success with the BU-3DFE database.

The approach developed by Kakadiaris et al. (2006) presented a 3D face recognition system based on a Deformable Model Fitting: An Annotated Face Model and a Haar wavelet analysis. An alignment of the face is done after the pre-processing of the image using the median cut, the hole filling, and the subsampling methods. They used the spin image algorithm to expect the rotation and the translation of the face. Then, an Iterative Closest Point algorithm is used for the alignment of the 3D face. Finally, the Enhanced Simulated Annealing technique is applied on the deformable data to ensure that the face is correctly aligned. So, the experimental results present a 97.3% recognition rate with the full FRGC v2 Database.

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