Abstract
Face Recognition has become a heavily studied field of AI. Competing techniques have been proposed, both holistic and local, each has their own advantages and disadvantages. Recently, a unified methodology using a Regional Voting framework has improved the accuracy of all holistic algorithms significantly and is currently regarded as one of the best approaches. In this work, based on the success of regional voting, we developed a two layer voting system called Weighted Regional Voting Based Ensemble of Multiple Classifiers (WREC), which can embed all available face recognition algorithms. The first layer embeds a holistic algorithm into a Regional Voting framework. The second layer gathers the classification results of different algorithms from the first layer and then makes the final decision. Extensive experiments carried out on benchmark face databases show the proposed system is faster and more accurate than several other leading algorithms/approaches in every case.
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Cheng, J., Chen, L. (2014). A Weighted Regional Voting Based Ensemble of Multiple Classifiers for Face Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_46
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DOI: https://doi.org/10.1007/978-3-319-14364-4_46
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
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