Multi-pose Face Recognition Using Fusion of Scale Invariant Features
This paper presents a new multi-pose face recognition approach using fusion of scale invariant features (FSIF). The FSIF is a face descriptor representing 3D face images features which is created by fusing some scale invariant features extracted by scale invariant features transforms (SIFT) from several different poses of 2D face images. The main aim of this method is to avoid using 3D scanner for estimating any pose variations of a face image but it still have reasonable achievement compare to 3D-based face recognition method for multi-pose face recognition. The experimental results show the proposed method is sufficiently to overcame large face variability due to face pose variations.
KeywordsFace Recognition Linear Discriminant Analysis Recognition Rate Face Image Scale Invariant Feature Transform
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- 4.Cuicui, Z., Uchimura, K., Zhang, C., et al.: 3D Face Recognition Using Multi-level Multi-feature Fusion. In: Proceedings of the 4th Pacific-Rim Symposium on Image and Video Technology (PSIVT 2010), Singapore, pp. 21–26 (2010)Google Scholar
- 6.Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: Proceedings of the International Conference on Computer Vision, Corfu (1999)Google Scholar
- 7.Wijaya, I.G.P.S., Uchimura, K., Hu, Z.: Improving the PDLDA Based Face Recognition Using Lighting Compensation. In: Proceedings of Workshop of Image Electronics and Visual Computing 2010, Nice France, CDROM (2010)Google Scholar
- 8.Samaria, F., Harter, A.: Parametrization of a stochastic model for human face identification. In: The 2nd IEEE Workshop on Applications of Computer Vision, Sarasota Florida, pp. 138–142 (1994)Google Scholar