A novel decorrelated neural network ensemble algorithm for face recognition

The main purpose of negative correlation learning (NCL) is to produce ensembles with sound generalization capability through controlling the disagreement among base learners’ outputs. This paper uses neural networks with random weights (NNRWs) to implement such learning scheme in the study of face recognition. Particularly, two-dimensional (2D) feed-forward neural networks (2D-FNNs) with random weights (2D-NNRWs) are employed as base components, and which are incorporated with the NCL strategy for building neural network ensembles, where the basis functions of the base networks are generated randomly and the free parameters of the 2D-FNNs can be determined by solving a linear equation system. Also, an analytical solution is derived for these parameters. To examine the merits of the proposed algorithm, a series of comparative experiments are performed. The experimental results indicate that the proposed approach outperforms existing approaches.