Biometric systems for recognizing faces in video streams have become relevant in a growing number of private and public sector applications, among them screening for individuals of interest in dense and moving crowds. In practice, the performance of these systems typically declines because they encounter a variety of uncontrolled conditions that change during operations, and they are designed a priori using limited data and knowledge of underlying data distributions. This paper presents multi-classifier system that can achieve a high level of performance in real-world video surveillance applications. This system assigns an ensemble of detectors (2-class classifiers) per individual, where base detectors are co-jointly trained using population-based evolutionary optimization. During enrolment of an individual, an aggregative Dynamic Niching Particle Swarm Optimization (DNPSO)-based training strategy generates a diversified homogenous pool of ARTMAP neural network classifiers using reference data samples. Classifiers associated with local optima of the aggregative DNPSO are directly selected and efficiently combined in the Receiver Operating Characteristic (ROC) space. Performance is assessed in terms of both accuracy and resource requirements on facial regions extracted from video streams of the Face in Action database. A comparison between a standard global and modular classification architectures is provided in this paper. Simulation results indicate that recognizing an individual using the aforementioned ensemble of detectors provides a scalable architecture that maintains a significantly higher level of accuracy and robustness as the number of individuals grows.
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