A comparison of features spaces for face recognition problem

OZYER, Gulsah Tumuklii
Akbaş, Emre
Yarman Vural, Fatoş Tunay
One of the most important problems in face recognition problem is designing the feature space which represents human face the "best". Concatenating the popular feature sets together and forming a high dimensional vector introduces the curse of dimensionality problem. For this reason, feature selection is required in order to reduce the dimension of the feature space. In this study, popular feature sets used in face recognition literature are considered and comparison between these sets is done. Furthermore, high dimensional space which is obtained by concatenating all the available features is reduced to a lower dimensional space by using the minimum redundancy maximum relevance feature selection method. ORL and UMIST face databases are used in experiments


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An important problem in face recognition is the design of the feature space which represents the human face. Various feature sets have been and are continually being proposed for this purpose. However, there exists no feature set which gives a superior and consistent recognition performance on various face databases. Concatenating the popular features together and forming a high dimensional feature space introduces the curse of dimensionality problem. For this reason, dimensionality reduction techniques suc...
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Citation Formats
G. T. OZYER, E. Akbaş, and F. T. Yarman Vural, “A comparison of features spaces for face recognition problem,” 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46065.