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Face recognition includes a set of challenges like expression variations, occlusions of facial parts, similar identities, resolution of the acquired images, aging of the subjects and many others. Among all these challenges, most of the face recognition techniques have evolved in order to overcome two main problems: illumination and pose variation as described by Phillips et al. [1] as well as Zhao and Chellapa [2]. Either of these influences can cause serious performance degradation in a face recognition system. Pose can change the appearance of an object drastically, and in the most of the cases these differences induced by pose variations are larger than differences between individuals, what makes the recognition task difficult. The same statement is valid for illumination variation. Therefore, pose and illumination (among other challenges) are the main causes for the degradation of 2D face recognition algorithms.
A recent public test at a German central train station showed only a recognition rate of 30% with a training set of 200 people out of 23000 people as described in the report of Pretzel and Lotz [3].
[1] Phillips, P.J., Grother, P., Micheals, R., Blackburn, D., Tabassi, E., Bone, J.: Face Recognition Vendor Test 2002: Evaluation Report. Technical Report NISTIR 6965, National Institute of Standards and Technology (2003)
[2] Zhao, W., Chellapa, R.: Face Processing: Advanced modeling and methods. Academic Press (2006)
[3] Pretzel, Lotz: Research project: Face recognition as a search tool. Technical report, Bundeskriminalamt Wiesbaden (2007)