The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy cyclic random mapping for face recognition based on MD-RiuLBP feature
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Theories and applications of LBP: a survey
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
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The LBP (Local Binary Pattern) feature is one of the dominant methods in face recognition. The growth of sampling density of general Uniform LBP operator will improve the precision with the cost of very high-dimensional feature in face recognition. The normally adopted Riu-LBP(Rotation Invariant Uniform LBP) feature is of very low dimension but deteriorate precision due to the lose of direction information. In this paper, we propose feature-level fusion of multidirectional Riu-LBP features. With such simple scheme, the feature dimension is drastically decreased while the precision is comparable or even better than the general Uniform LBP features.