Adaptive Histogram Normalization based Loss Function in Deep Learning Algorithm for Face Recognition

To improve the recognition accuracy and to solve the overfitting problem of traditional face recognition methods, this paper proposed an adaptive histogram normalization algorithm to reduce brightness effect in training data and designing loss function. The proposed algorithm can adaptive adjustment training images and inference parameters based on the real-time captured images data. In experimental results, the proposed algorithm has higher accuracy than other algorithms and has higher testing accuracy to improve overfitting.