In order to solve the problems that the accuracy is low or the head pose estimation cannot be performed by traditional head pose estimation methods when the key feature points of the face cannot be located due to partial occlusion or too large angle, a multi-angle head pose estimation method based on optimized LeNet-5 network was proposed. Firstly, the depth, the size of the convolution kernel and other parameters of the Convolutional Neural Network (CNN) were optimized to better capture the global features of the image. Then, the pooling layers were improved, and a convolutional operation was used to replace the pooling operation to increase the nonlinear ability of the network. Finally, the AdaBound optimizer was introduced, and the Softmax regression model was used to perform the pose classification training. During the training, hair occlusion, exaggerated expressions and wearing glasses were added to the self-built dataset to increase the generalization ability of the network. Experimental results show that, the proposed method can realize the head pose estimation under multi-angle rotations, such as head up, head down and head tilting without locating key facial feature points, under the occlusion of light, shadow and hair, with the accuracy of 98.7% on Pointing04 public dataset and CAS-PEAL-R1 public dataset, and the average running speed of 22-29 frames per second.

In order to prevent photo and video attacks in the face recognition system, an interactive liveness detection algorithm was proposed which combines the head pose and facial expression. Firstly, the number of convolution kernels, network layers, and regularization of VGGNet were adjusted and optimized, and a multi-layer convolutional head pose estimation network was constructed. Secondly, the methods such as global average pooling, local response normalization and convolutional replacement pooling were introduced to improve VGGNet and build an expression recognition network. Finally, the above two networks were fused to realize an interactive liveness detection system, which sends random instructions to users to complete liveness detection in real time. The experimental results show that the proposed head pose estimation network and expression recognition network achieve 99.87% and 99.60% accuracy on CAS-PEAL-R1 dataset and CK+ dataset respectively, and the liveness detection system has the comprehensive accuracy reached 96.70%, the running speed reaches 20-28 frames per second, which make the generalization ability of the system outstanding in the practical application.

In order to effectively reduce the energy consumption of Wireless Sensor Network (WSN) and extend the life cycle of the network, Low Energy Adaptive clustering Hierarchy (LEACH) and other clustering routing protocols were analyzed. For improving their weaknesses, a Clustering Routing algorithm based on Attraction factor and Hybrid transmission (CRAH algorithm) was proposed. Firstly, in order to solve the problem of unreasonable selection of Cluster Head (CH) nodes, the node residual energy and the node location were combined as a new index of CH nodes selection by adopting the method of weighted sum. Then, the tasks of the CH nodes were reassigned, and new fusion nodes were chosen. The fusion nodes sent data to Base Station (BS) according to a hybrid of single hop and multiple hops, and combined attraction factor and the Dijkstra algorithm to present a new algorithm, Attraction Factor-Dijkstra (AF-DK) algorithm was proposed with the combination of attract factor and Dijkstra algorithm for finding the optimal paths for fusion nodes. The simulation results show that, compared with the protocols of LEACH, LEACH-Centralized (LEACH-C) and Hybrid Energy-Efficient Distributed clustering (HEED), the CRAH algorithm improved the network lifetime by about 51.56%, 47.1% and 42% respectively, and slowed the network energy consumption significantly. The amount of data receiving by Base Station (BS) decreased 69.9% in average. The CRAH algorithm makes CH selection more reasonable, effectively reduces the redundant data in the process of communication, balances the network energy consumption, and extends the life cycle of the network.