Local binary patterns for face recognition under varying variations
Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research
Multi-expression face recognition using neural networks and feature approximation
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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A multiblock-fusion scheme for face recognition is proposed in this paper. Three face recognition algorithms, i.e. probabilistic match, Linear Discriminant Analysis (LDA) and Discrete Cosine Transform (DCT) are compared under the fusion strategy. By combining global and local features, the multiblock-fusion enhances the robustness against variations of illumination, facial expressions and pose. Different partitions and combinations show specific performancefor each method. The experimental results demonstrate that the fusion outperforms the single method. Some other characteristics of the three methods are also verified by the experiments.