Analysis of euclidean distance and Manhattan Distance measure in face recognition

The face expression recognition problem is challenging because different individuals display the same expression differently [1].Here PCA algorithm is used for the feature extraction. Distance metric or matching criteria is the main tool for retrieving similar images from large image databases for the above category of search. Two distance metrics, such as the L1 metric (Manhattan Distance), the L2 metric (Euclidean Distance) have been proposed in the literature for measuring similarity between feature vectors. In content-based image retrieval systems, Manhattan distance and Euclidean distance are typically used to determine similarities between a pair of image [2]. Here facial images of three subjects with different expression and angles are used for classification. Experimental results are compared and the results show that the Manhattan distance performs better than the Euclidean Distance.