Combining Generative and Discriminative Learning for Face Recognition

Face recognition is a very complex classification problem and most existing methods are classified into two categories: generative classifiers and discriminative classifiers. Generative classifiers are optimized for description and representation which is not optimal for classification. Discriminative classifiers may achieve less asymptotic errors but are inefficient to train and may overfit to training data. In this paper, we present a hybrid learning algorithm that combines both generative learning and discriminative learning to find a trade-off between these two approaches. Experiments on Asian Face Database show a reduction in classification error rate for our hybrid learning method.

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