Incremental adaptation of fuzzy ARTMAP neural networks for video-based face classification

In many practical applications, new training data is acquired at different points in time, after a classification system has originally been trained. For instance, in face recognition systems, new training data may become available to enroll or to update knowledge of an individual. In this paper, a neural network classifier applied to video-based face recognition is adapted through supervised incremental learning of real-world video data. A training strategy based on particle swarm optimization is employed to co-optimize the weights, architecture and hyperparameters of the fuzzy ARTMAP network during incremental learning of new data. The performance of fuzzy ARTMAP is compared under different class update scenarios when incremental learning is performed according to 3 cases-(A) hyperparameters set to standard values, (B) hyperparameters optimized only at the beginning of the learning process with all classes, and (C) hyperparameters re-optimized whenever new training data becomes available. Overall results indicate that when samples from each individual enrolled to the system are employed for optimization, a higher classification rate is achieved and the solutions produced are more robust to variations caused by pattern presentation order. When all classes are refined equally, this is true with incremental learning according to case (C), whereas, if one class is refined at a time, best performance is obtained with case (B). However, optimizing hyperparameters requires more resources: several training sequences are needed to find the optimal solution and fuzzy ARTMAP with hyperparameters optimized according to classification rate tends to generate a high number of category nodes over longer convergence time.