Binary Cuckoo Search–Based Feature Selection for Multi-Algorithmic and Multimodal Biometric Authentication

Feature-level fusion in multi-algorithmic and multimodal biometric systems produces very high-dimensional feature spaces that significantly degrade classification performance, increase computational and memory requirements, and reduce system scalability. This paper presents a binary Cuckoo Search (CS)–based feature selection framework designed to identify optimal feature subsets from fused biometric feature vectors while maintaining high recognition accuracy. The proposed approach encodes each feature subset as a binary nest, employs Lévy-flight–driven global exploration, implements local random walk mechanisms, and optimizes a wrapper-based fitness function combining recognition accuracy and feature subset size. Experimental evaluations on multi-algorithmic fingerprint, iris, and palmprint systems using public databases (CASIA V1.0, IITD V1.0, FVC, and others) demonstrate that CS-based feature selection substantially improves recognition rates compared to Principal Component Analysis (PCA) while achieving 80–91% feature-space reduction across various multi-algorithmic and multimodal configurations. Results show recognition accuracy improvements from 80–85% (PCA baseline) to 89–98% (proposed CS-FS) with Euclidean distance matching, and up to 99% accuracy with supervised classifiers. The method outperforms traditional dimensionality reduction techniques and exhibits competitive or superior performance relative to other evolutionary feature selection approaches such as Genetic Algorithms and Particle Swarm Optimization.