Face image retrieval systems have attained much importance in recent times, due to many real time application demands it. Normally, image retrieval has become a challenging issue in the real world applications because the image collections are often high dimensional. This higher dimensionality results in a degradation of the classification accuracy of the classifier. Face recognition is the most important process of the face retrieval systems. The major step of face recognition is feature extraction and classification. The performance of a face recognition system highly depends on the way in which the features are extracted and the classification of them to the appropriate group. By providing better dimensionality reduction and higher class discrimination prior to the classification process, a classifier must provide higher classification accuracy. So, dimensionality reduction has a significant role in the classification process. This preprocessing step must provide a better dimensionality reduction of high dimensional data as well as high discrimination between classes. In this paper an attempt has been made to summarize some of the existing dimensionality reduction methods and its relevance to improve the classification accuracy of the existing face retrieval systems. The proposed method has improved the classification accuracy into a higher rate.

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Face image retrieval Dimensionality reduction PCA LDA SVM