Scale Invariant Feature Transform (SIFT) is a method for extracting distinctive invariant feature from images. SIFT has been applied to many problems such as face recognition and object recognition. The authors have analyzed performance of SIFT using Euclidean distance as a matching algorithm. Further the matching rate can be enhanced/improved by changing distance calculation methods used for matching between two face images. So this paper also describes face recognition under various distance calculation methods like Correlation and Cosine. The experiments are conducted on different images of ORL face database and Indian Face database by changing illumination condition, scaling and rotation. From the experiments, it is shown that cosine and correlation distance calculation methods have performed well compared to the Euclidean distance matching method of original SIFT.