A MULTI BIOMETRIC SYSTEM BASED ON HYBRID DENSENET 201-3D CNN CLASSIFIES AND AQUILA OPTIMIZATION ALGORITHM FOR FINGERPRINT-IRIS-FACE

Abstract

Biometric emerging and promising technology for identifying and validating a person. It is very strong, and it is accurate. Hard to copy, model, share, it distributes and cannot be stolen or forgotten. Combining more than one biometric the feature offers a promising solution to provide additional security. The traditional method of a person's identity and identity are determined through use of Biometric-Technology. The main purpose of this research project is to design and suggest an in-depth learning model: 3D convolutional neural network (3D-CNN) a category based on multiple biometric fingerprints, face recognition system and iris. To remove the feature, Densenet-201 was used. To improve functionality, feature level integration is used. Suggested Aquila Optimizer tuning separator parameter to determine the efficiency of the proposed model. Test results look at different parameters like this Equal Error Rate (EER), False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy showing the performance of the proposed model better there are other development models, which prove that the required identity is real or fake.