Face recognition has become a field of interest in pattern recognition and artificial intelligence. One of the vital steps involved in face recognition is that of ‘Feature Extraction’. Feature extraction is imperative because handling data whose dimensions are inherently high, is rather a tedious process and therefore we adopt strategies for the purpose of dimensionality reduction. This process of studying data by reducing dimensions is called subspace analysis. Two such subspace methods are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA extracts the most significant components or those components which are more informative and less redundant, from the original data. While LDA is used to find a linear combination of features that characterizes or separates two or more classes in the data. Both PCA and LDA are studied in this paper. For our data set, distance measure is used as a classifier. Euclidean distance, Manhattan distance, Chi square distance are some examples for distance measures.

Face recognition, Feature extraction, Dimensionality reduction, Subspace methods, PCA, LDA, Classification

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