Adaptive multi-classifier systems for biometric recognition

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Biometric recognition of individuals provides a powerful alternative to traditional authentication schemes that are presently applied in many security and surveillance systems. In practice, the performance of biometric systems typically declines because they face complex environments that change during operations, and they are designed a priori using limited data and knowledge of underlying data distributions. Biometric models are often poor representatives of the biometric trait to be recognized. For accurate recognition, these models should be adapted over time in response to new or changing input features, data samples, priors, classes and environments. This research program seeks to investigate adaptive multi-classifier systems (AMCSs) that can achieve a high level of performance in real-world biometric applications, and efficiently update biometric models in response to emerging information from the operational environment. These AMCSs evolve an ensemble of binary classifiers (EoCs) per individual, where classifiers are co-jointly trained using population-based evolutionary optimization. During the enrolment of an individual to system, a new dynamic multi-objective PSO-based training strategy generates a diversified pool of base classifiers through batch learning of data samples. Then, in response to new data for that individual, this strategy either generates an additional pool for combination with previously-learned classifiers, or evolves the pool of previously-learned classifiers through incremental learning. A subset of classifiers is then selected from an individual's pool according to specialized measures of accuracy and diversity. New incremental Boolean combination techniques are employed to adapt decision-level fusion functions over time, in response to new or changing pools. To account for limited data and skewed distributions, incremental BC is applied in ROC or other spaces. Although the robust adaptive techniques described in this proposal can be applied to a wide range of applications, face recognition, signature verification and biometric fusion are the focus of this research. To accelerate all steps of this program, new AMCSs will be validated with real biometric data on high-speed GPGPU platforms.