Abstract:
Most biometric systems that are presently used in real time applications typically use a single biometric characteristic to authenticate the user. A biometric system which is based only on a single biometric identifier in making a personal identification is often not able to meet the desired performance requirements. Multimodal biometrics is an emerging field of biometric technology, where more than one biometric trait to improve the combined performance. We design a bimodal biometric system which integrates FKP and Face. Hence this paper has focused on the extraction of features from FKP and Face using Scale Invariant Feature Transform (SIFT), and the key points are derived from FKP and Face and then they are clustered using K-Means Algorithm. The centroid, mean and variance of key extracted image of K-Means are stored in the database which is compared with the query FKP and Face features to prove the recognition and authentication. The comparison is based on the XOR operation. Results are performed on the Poly-U FKP and Face database to check the proposed FKP and Face recognition method. It can be used to overcome some of the limitations of a single biometrics, increases the performance.

Keywords: Biometric, SIFT Algorithm, Feature Extraction, K-Means Algorithm