Face liveness detection is an important part of the face recognition process, and is particularly important for the security of identity verification. In view of the cheating methods such as photo, video, mask, hood and head model in the face recognition process, the RGB map and depth map information of the face was collected by the Intel Realsense camera, and a lightweight liveness detection of feature fusion was proposed based on MobileNetV3 to fuse the features of the depth map and the RGB map together and perform the end-to-end training. To solve the problem of large parameter quantity in deep learning and the distinction of the weight areas by the network tail, the method of using Streaming Module at the network tail was proposed to reduce the quantity of network parameters and distinguish weight regions. Simulation experiments were performed on CASIA-SURF dataset and the constructed CQNU-LN dataset. The results show that, on both datasets, the proposed method achieves an accuracy of 95% with TPR@FPR=10E-4, which is increased by 0.1% and 0.05% respectively compared to ShuffleNet with the highest accuracy in the comparison methods. The accuracy of the proposed method reaches an accuracy of 95.2% with TPR@FPR=10E-4 on the constructed CQNU-3Dmask dataset, which is improved by 0.9% and 6.5% respectively compared to those of the method training RGB maps only and the method training depth maps only. In addition, the proposed model has the parameter quantity of only 1.8 MB and FLoating-point Operations Per second (FLOPs) of only 1.5×10 ⁶. The proposed method can perform accurate and real-time liveness detection on the extracted face target in practical applications.

Existing Word2vec-based Network Representation Learning (NRL) algorithms use a Random Walk (RW) to generate node sequence. The RW tends to select nodes with larger degrees, so that the node sequence can not reflect the network structure information well, decreasing the performance of the algorithm. To solve the problem, a new network representation learning algorithm based on improved random walk was proposed. Firstly, RLP-MHRW (Remove self-Loop Probability for Metropolis-Hastings Random Walk) was used to generate node sequence. This algorithm would not favor nodes with larger degrees while forming a node sequence, so that the obtained sequence can efficiently reflect the network structure information. Then, the node sequence was put into Skip-gram model to obtain the node representation vector. Finally, the performance of the network representation learning algorithm was measured by a link prediction task. Contrast experiment has been performed in four real network datasets. Compared with LINE (Large-scale Information Network Embedding) and node2vec on arXiv ASTRO-PH, the AUC (Area Under Curve) value of link prediction has increased by 8.9% and 3.5% respectively, and so do the other datasets. Experimental results show that RLP-MHRW can effectively improve the performance of the network representation learning algorithm based on Word2vec.

This paper presents an approach bases on ant colony optimization route algorithm of mobile Ad Hoc networks. It can effectively bear network load in Ad Hoc networks by using the self-adaptability of that ant colony algorithm. The simulations in NS-2 show that it performs very well on Ad Hoc environments, especially in throughput, average latency, and delivery ratio. The performance is better than that of Ad Hoc On-demand Distance Vector routing (AODV) and Dynamic Source Routing (DSR).