Generalization in Pattern Recognition with Complete Feature Set

The complete feature set is one of the challenging topics in the pattern recognition field. Lots of researches are taken on how to obtain the robust features which can be transferred into different kinds of applications, i.e. an excellent generality. However, those features in the state-of-the-art recognition model are usually redundant. The internal modeling errors in network architecture, and the contaminated training samples, leave the features beyond Complete. Aiming at the completeness problem, this paper presents a complete feature point of view. A group of decoupled features are obtained by the adversarial learning framework. Then, these features are used to construct the complete feature set, which not only can be used in a limited source scenario, but also perform well in cross dataset verification. Then, the proposed method is examined in applications of knowledge distillation and cross domain facial recognition task. The experimental results demonstrate an effectiveness of the proposed method. The construction of complete feature set can bring a more robust pattern recognition model.