| Abstrakt: |
We investigated personal face identification systems using deep learning networks and we proposed a deep neural network architecture for improving false positive rate. Most of conventional face identification systems have the same number of output nodes as the number of faces that can be identified, and each node is trained to identify one registered face. In this paper, we added an extra node to identify unregistered faces for improving false positive rate(FPR) and accuracy by reducing the propagation of matching probabilities toward other output nodes identifying the registered faces, when unregistered faces are input. The proposed model has been trained with the VGGFace2 dataset. The performance of the proposed model was analyzed in terms of accuracy, precision, FPR, and false negative rate (FNR), and was compared to that of the existing model. According to the performance analysis results, when FNR is 5%, the FPR of the proposed model is improved by about 83% compared to the existing model. [ABSTRACT FROM AUTHOR] |
