Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
To simulate the firing pattern of biological grid cells, this paper presents an improved computational model of grid cells based on column structure. In this model, the displacement along different directions is processed by modulus operation, and the obtained remainder is associated with firing rate of grid cell. Compared with the original model, the improved parts include that: the base of modulus operation is changed, and the firing rate in firing field is encoded by Gaussian-like function. Simulation validates that the firing pattern generated by the improved computational model is more consistent with biological characteristic than original model. Besides, the firing pattern is badly influenced by the cumulative positioning error, but the computational model can also generate the regularly hexagonal firing pattern when the real-time positioning results are modified. 相似文献