A New Innovation in Biometric Recognition Using Agentic AI Based on Swarm Feature Engineering with Ensemble LenetCNN

Recent developments in biometric recognition analysis include various imaginative intelligence concepts for identifying multimodal friction rides based on feature descriptions to solve the complexities in security and person identification applications. For information security, the Research uses multimodal screening analyses from finger veins, finger knuckles, iris, palm prints, and fingerprints. Most traditional methodologies only concentrate on object region regions, which fails to identify the feature relation and entity variation. The problem arises from the multi-correlation feature dimension, which increases the non-relation image registration and structural properties in entity resolution. Due to higher degradation in entity resolution, finding ridges creates false occlusion, causing a higher dimensionality ratio, lower precision rate, recall rate, and more false positives to provide lower identification accuracy. To resolve this problem, we propose an artificial intelligence based on AI-powered Multimodal biometrics recognition using deep scalar feature engineering with Optimal Particle Swarm Intelligence (OPSI) feature selection with Hyper capsule Gated LenetCNN. Initially, the multimodal biometric dataset is pre-processed with an adaptive Gaussian mean filter to normalize the images, and the Iterative pattern Slice Fragment Clustering (IPSFC) is applied for entity markings. Then, a vector Pyramid Scene Parsing Network is used for segmentation. Then, to reduce the feature dimension, the Optimal Particle Swarm Intelligence (OPSI) feature selection is applied to minimize the non-relation features. Then, LSTM- Hyper Capsnet CNN is used to identify the biometric data. Our methodology involves extracting pertinent biometric data features and utilizing deep learning algorithms to enhance identification accuracy. The proposed deep optimal feature engineering approach is introduced, allowing the model to prioritize and select features that significantly increase the identification accuracy while minimizing feature redundancy. The output result is evaluated, and improved accuracy, sensitivity, specificity, and F1 measure performance are achieved, significantly increasing the precision rate based on the proposed CNN classification.