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Three-dimensional (3D) facial recognition and prediction

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Abstract

This paper provides solution to the problem in identifying humans from their 3D facial characteristics. For this reason, a standard 3D facial recognition system was built and used in this research work. This work proposes two novel fusion schemes where the first one employs a confidence-aided combination approach, and the second one implements a two-level serial integration method. Recognition simulations performed on the 3DRMA and the FRGC databases show that: (1) generic face template-based rigid registration of faces is better than the non-rigid variant, (2) principal curvature directions and surface normal have better discriminative power, (3) representing faces using local patch descriptors can both reduce the feature dimensionality and improve the identification rate, and (4) confidence-assisted fusion rules and serial two-stage fusion schemes have a potential to improve the accuracy when compared to other decision-level fusion rules.

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities under Grant No. 30920140111004, and the National Natural Science Foundation of China under Grant No. 81070743/81001427.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Idowu Paul Okuwobi, Qiang Chen, Sijie Niu & Loza Bekalo

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  1. Idowu Paul Okuwobi
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  2. Qiang Chen
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  3. Sijie Niu
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  4. Loza Bekalo
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Correspondence to Idowu Paul Okuwobi.

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Okuwobi, I.P., Chen, Q., Niu, S. et al. Three-dimensional (3D) facial recognition and prediction. SIViP 10, 1151–1158 (2016). https://doi.org/10.1007/s11760-016-0871-z

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  • DOI: https://doi.org/10.1007/s11760-016-0871-z

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