Abstract: Our method An effective approach to frontal face recognition using distance measures will detect and then recognize the face by comparing characteristics of the face to those of known individuals. Present approach treats the face identification problem as an intrinsically two-dimensional (2-D) identification problem rather than requiring recovery of three- dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as eigenfaces, because they are the eigenvectors (principal components) of the set of faces, they do not necessarily correspond to features such as eyes, ears and noses. The projection operation characterizes an individual face in a weighted sum of the eigenface feature and so to recognize a particular face it is necessary only to compute these weights to those of known individuals. Some particular advantages of our approach are that using only a weighted sum of these eigenfaces, it is possible to reconstruct each face in the data set. At present existing method recognize faces using Euc lidean distance measure. But in present system we used various distance measures such as Euclidean, chess board a nd city block distanc e measures. In our method City block distance which gives better results as compared to Euclidean and chess board distance measures.