Abstract
In this paper, an approach that uses a combination of neural network classifiers (CNNC) is applied to human face recognition. We present a divide-and-conquer approach for system composed of several separate networks. Decomposing the complex problem into sub-problems for solving them by a binary base classifier is presented. Each of that learns to recognize a subject of the complete set of training database. Combining the results of sub-problems with max rule accomplished to achieve better performance. The recognition rate of 100% for ORL and Yale database was obtained using the mentioned devised algorithm.
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Ebrahimpour, R., Ehteram, S.R., Kabir, E. (2005). Face Recognition by Multiple Classifiers, a Divide-and-Conquer Approach. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_33
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DOI: https://doi.org/10.1007/11553939_33
Publisher Name: Springer, Berlin, Heidelberg
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