This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropy-based measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem. © 2011 Springer-Verlag.
CITATION STYLE
Kyperountas, M., Tefas, A., & Pitas, I. (2011). Entropy-based iterative face classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6583 LNCS, pp. 137–143). https://doi.org/10.1007/978-3-642-19530-3_13
Mendeley helps you to discover research relevant for your work.