This paper is based on the recognition of faces using string matching. The approximate string matching is a method for finding an approximate match of a pattern within a string. Exact matching is impracticable for a larger amount of data as it involves more time. Those issues can be solved by finding an approximate match rather than an exact match. This paper aims to experiment with the performance of approximation string matching approaches using various distance measures such as Edit distance, Longest Common Subsequence (LCSS), Hamming distance, Jaro distance, and Jaro-Winkler distance. The algorithms generate a near-optimal solution to face recognition system with reduced computational complexity. This paper deals with the conversion of face images into strings, matching those image strings by using the approximation string matching algorithm that determines the distance and classifies a face image based on the minimum distance. Experiments have been performed with FEI and ORL face databases for the evaluation of approximation string matching algorithms and the results demonstrate the utility of distance measures for the face recognition system.