Abstract
Gabor wavelet-based methods have been proven that are useful in many problems including face detection. It has been shown that these features tackle well facing into image recognition. In image identification, while there is a number of human faces in a repository of employees, it is aimed to identify the face of an arrived employee is which one? So the application of gabor wavelet-based features is reasonable. We propose a weighted majority average voting classifier ensemble to handle the problem. We show that the proposed mechanism works well in an employees' repository of our laboratory. © 2011 Springer-Verlag.
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CITATION STYLE
Parvin, H., Mozayani, N., & Beigi, A. (2011). A classifier ensemble for face recognition using gabor wavelet features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6694 LNCS, pp. 301–307). https://doi.org/10.1007/978-3-642-21323-6_38
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