Continuous user authentication using multimodal biometric traits with optimal feature level fusion

The biometric process demonstrates the authenticity or approval of an individual in view of his/her physiological or behavioural characteristics. Subsequently, for higher security feature, the blend of at least two or more multimodal biometrics (multiple modalities) is requiring. Multimodal biometric technology gives potential solutions for continuous user-to-device authentication in high security. This research paper proposed continuous authentication (CA) process using multimodal biometric traits considers finger and iris print images to various feature extraction process. At that point, features are extracted into optimal feature level fusion (FLF) process. The final feature vector is acquired by concatenating directional information and centre area features. Disregard the optimal feature process the inspired fruit fly optimisation (FFO) model is considered, and then these model is fused into authentication procedure to find the matching score values (Euclidian distance) with imposter and genuine user. From the approach, results are accomplished most extreme accuracy, sensitivity and specificity compared with existing papers with better FPR and FRR value for the authentication process. The result shows 92.23% accuracy for the proposed model when compared to GA, PSO which is attained in MATLAB programming software.