For authentication purposes, the identification and verification of a user is done by biometric traits like finger print, face, iris and gait, etc. Among the various traits finger print is mostly used in commercial applications for recognizing user's identity. The other hand based modalities such as vein, and finger knuckle are gaining importance. This paper proposes a methodology for secure biometrics authentication using Finger Knuckle Print (FKP). The texture patterns from finger knuckle are extracted using Gabor with Exception-Maximization (EM) algorithm and the feature vectors from these texture patterns are acquired using Scale Invariant Feature Transform (SIFT) algorithm. The main focus is to reduce the false rejection rate without increasing the false acceptance rate and to improve the performance over the conventional hand based modalities. The performance is compared with Genuine Acceptance Rate (GAR) and False Rejection Rate (FRR). One of the advantages of FKP authentication is its user friendliness in data collection.