Scale Invariant Feature Transform (SIFT) is a method for extracting distinctive invariant feature from images [1]. SIFT has been applied to many problems such as face recognition and object recognition [18], [19], [20], [21]. We 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 [17] and Indian Face database [16] 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.

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.