A Comprehensive Review of Swarm- and Evolutionary-Based Feature Selection Techniques for Multimodal Biometric Recognition

Reliable and robust personal authentication technologies have become indispensable in modern digital and physical security infrastructures. Traditional unimodal biometric systems—using a single biometric trait—often suffer from noise, spoofing vulnerabilities, and intra-class variability. To overcome these limitations, multimodal biometric systems fuse evidence from multiple biometric sources. However, feature-level fusion, despite yielding richer discriminatory information, produces high-dimensional feature spaces that demand efficient dimensionality reduction or feature selection. This review presents a consolidated analysis of three optimization-driven multimodal biometric recognition systems: Particle Swarm Optimization (PSO) for fingerprint–palmprint fusion, Genetic Algorithm (GA) for iris–fingerprint fusion, and Artificial Bee Colony (ABC) optimization for iris–palmprint fusion. We critically examine preprocessing techniques, feature extraction schemes, fusion strategies, dimensionality-reduction approaches, classifier performance, and comparative advantages. The review highlights trends, challenges, and future research directions in optimization-enhanced multimodal biometrics.

Multimodal biometrics, Feature-level fusion, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony, Dimensionality reduction, Palmprint, Fingerprint, Iris recognition, Machine learning

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