A research on face cognition method with deep ensemble learning and feedback mechanism

Face recognition technology is an important research field for deep learning. In order to overcome the shortcomings of traditional open-loop face cognition mode and deep neural network structure, and to imitate human cognition model with real-time evaluation of cognitive results and self-optimized regulation of feature space and classification criteria, drawing on the theory of closed-loop control theory, this paper explores a face intelligent cognition method with deep ensemble learning and feedback mechanism. Firstly, based on the DEEPID neural network, a multi-layer feature space of face images from the global to the local is established. Secondly, based on feature separability evaluation and variable precision rough set theory, a face cognition decision information system model with dynamic feature representation is established from the perspective of information theory, to compact the multilayer feature space. Thirdly, the ensemble random vector functional-link net is used to construct the classification criterion for the compacted multi-layer feature space. Finally, the entropy measure index of face recognition results is constructed to provide a quantitative basis for the self-optimization adjustment mechanism of face feature space and classification criteria. The experimental results show that the proposed method can effectively improve the recognition rate of face images compared with the existing methods.