Study on Face Recognition Based on Improved CCN and Ensemble Learning

In order to avoid overfitting of face recognition results in complex Convolutional Neural Network (CNN) on small and medium face situation, this paper provides a face recognition algorithm based on improved CNN and ensemble learning. Combining the characteristics of planar networks and residual networks, the improved CNN replaces the fully connected layer with the average pooling layer to make the network structure simple and highly portable. Based on this improved CNN, the voting-based ensemble learning strategy is used to implement convex combination for results of all individual learners and more accurate face recognition could be realized. The experimental results show that the face recognition accuracy of the proposed algorithm reaches to 99.67% on face databases with a high convergence speed.