A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory.

With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image.

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections.

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add "Salt and pepper" noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

The Effect of Safety Signs on the Monitoring of Conflict and Erroneous Response.

The safety sign is important in our daily life and workplace to prevent potential safety issues. However, it remains undetermined whether the safety signs would influence the cognitive control ability of the people, which serves to guide the behaviors in a goal-directed manner. Therefore, this study aimed to examine the effect of safety signs on cognitive control by uncovering the behavioral performance and neural manifestations underlying the monitoring of conflict and error. The participants performed a flanker task after watching low- and high-hazard safety signs with the electroencephalogram (EEG) data recorded continually. The behavioral results indicated a classic congruency effect with higher accuracy rate and faster response time under a congruent condition compared to an incongruent condition. However, no hazard effect on behavioral performances was observed. The results of event-related potentials (ERPs) demonstrated a more negative N2 elicited by the incongruent trials and an increased (error-related negativity) ERN difference between the error and correct responses in the high-hazard condition compared to those in the low-hazard condition, implying that the monitoring of the conflict and error were both enhanced after watching the high-hazard safety signs. This study contributes to the understanding of the relationship between safety signs and cognitive control, and further expand the measurements that can be applied to assess the effectiveness of safety signs design.

Decisions of the Dual-Channel Supply Chain under Double Policy Considering Remanufacturing.

Considering the preference of green consumers for remanufactured products, a dual-sale-channel supply chain model with government non-intervention, government remanufacturing subsidy policy, and carbon tax policy is constructed, respectively. The difference of the optimal decision between the firm and the government under the two policies is discussed in this paper. Meanwhile, we analyze the influence of green consumers on the government's optimal decision, based on social welfare maximization. It is found that without government intervention, social welfare is the lowest. The carbon tax policy is better when the proportion of green consumers and the environmental coefficient are extreme or moderate at the same time. Otherwise, the subsidy policy is better. The carbon tax policy is more effective than the subsidy policy in controlling carbon emissions. Profit-sharing contracts should be established by enterprises and governments to achieve win⁻win results.