Face Recognition, Geometric vs. Appearance-Based
In 2D face recognition, images are often represented either by their geometric structure, or by encoding their intensity values. A geometric representation is obtained by transforming the image into geometric primitives such as points and curves. This is done, for example, by locating distinctive features such as eyes, mouth, nose, and chin, and measuring their relative position, width, and possibly other parameters. Appearance-based representation is based on recording various statistics of the pixels’ values within the face image. Examples include: recording the intensities of the image as 2D arrays called templates and computing histograms of edge detectors’ outputs.
Face identification systems are challenged by variations in head pose, camera viewpoint, image resolution, illumination, and facial expression, as well as by longer-term changes to the hair, skin, and head’s structure. The geometric approach,...
- 1.Brunelli, R., Poggio, T.: Face recognition: Features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)Google Scholar
- 2.Kanade, T.: Picture processing system by computer complex and recognition of human faces. Ph.D. thesis, Kyoto University (1973)Google Scholar
- 3.Ding, L., Martinez, A.: Precise detailed detection of faces and facial features. In: Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 1–7 (2008)Google Scholar
- 5.Leung, M.K., Yang, Y.H.: Dynamic two-strip algorithm in curve fitting. Pattern Recognit. 23(1–2), 69–79 (1990)Google Scholar
- 6.Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. Int. J. Comput. Vis. 74(2), 167–181 (2007)Google Scholar
- 11.Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
- 12.Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. J Mach. Learn. Res. 36(1), 3–42 (2006)Google Scholar
- 13.Lanitis, A., Taylor, C.J., Cootes, T.F.: A unified approach to coding and interpreting face images. In: Proceedings of the International Conference on Computer Vision, pp. 368–374. IEEE Computer Society, Washington, DC, USA (1995)Google Scholar
- 14.Wiskott, L., Fellous, J.M., Kröger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)Google Scholar
- 15.Sivic, J., Everingham, M., Zisserman, A.: Person spotting: Video shot retrieval for face sets. In: 4th International Conference on Image and Video Retrieval, pp. 226–236 (2005)Google Scholar
- 17.Meyers, E., Wolf, L.: Using biologically inspired features for face processing. Int. J. Comput. Vis. 76(1), 93–104 (2008)Google Scholar
- 18.Huang, G., Jain, V., Learned-Miller, E.: Unsupervised joint alignment of complex images. Computer Vision, In: IEEE International Conference pp. 1–8 (2007)Google Scholar
- 19.Zhou, Y., Gu, L., Zhang, H.J.: Bayesian tangent shape model: estimating shape and pose parameters via bayesian inference. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–109–I–116 (2003)Google Scholar