The accuracy of authorship identification based on deep neural network decreases significantly when faced with a large number of candidate authors. In order to improve the accuracy of authorship identification, a neural network consisting of fast text classification (fastText) and an attention layer was proposed, and it was combined with the continuous Part-Of-Speech (POS) n-gram features for authorship identification of Chinese novels. Compared with Text Convolutional Neural Network (TextCNN), Text Recurrent Neural Network (TextRNN), Long Short-Term Memory (LSTM) network and fastText, the experimental results show that the proposed model obtains the highest classification accuracy. Compared with the fastText model, the introduction of attention mechanism increases the accuracy corresponding to different POS n-gram features by 2.14 percentage points on average; meanwhile, the model retains the high-speed and efficiency of fastText, and the text features used by it can be applied to other languages.

Portrait inpainting was widely used in the photo editing based on image rendering and computational photography. A lot of factors including the variety in clothing, different body types such as tall, short, fat and thin size, the high freedom degree of human body pose, bring difficulties to portrait inpainting. Therefore, an efficient portrait inpainting method based on Generating Adversarial Network (GAN) was proposed. The algorithm consists two stages. During the first stage, the image was roughly inpainted based on an encoder-decoder network, and then the body pose information in the image was estimated. During the second stage, the portrait was accurately inpainted based on the pose information and GAN. Besides, the key points of the portrait pose were connected by using portrait pose information to form the pose framework and perform the dilation operation, and the portrait pose mask was obtained. Thereby, a portrait pose loss function was constructed for network training. The experimental results show that: compared with the Contextual Attention inpainting method, the proposed method has the SSIM （Structural SIMilarity index） increased by one percentage point. The method, by adding the portrait pose information into the portrait inpainting process, effectively constrains the solution space range of portrait data in the zone to be inpainted, and strengthens the network's attention to the portrait pose information.

The traditional RGB-Depth (RGB-D) image object recognition methods have some drawbacks, such as insufficient feature learning and poor robustness of feature coding. In order to solve these problems, an object recognition method of RGB-D image based on Kernel Descriptor and Locality-constrained Linear Coding (KD-LLC) was proposed. Firstly, based on the kernel function of image block matching, several complementary kernel descriptors from RGB-D images, such as 3D shape, size, edges and color, were extracted using Kernel Principal Component Analysis (KPCA). Then, the extracted feature from different cues, were processed by using LLC and Spatial Pyramid Pooling (SPP) to form the corresponding image coding vectors. Finally, the vectors were combined to obtain robust and distinguishable image representation. As a hand-crafted feature method, the proposed algorithm was compared to other hand-crafted feature methods on a RGB-D image dataset. In the proposed algorithm, multiple cues from depth image and RGB image were used, and the sampling points selection and basis vectors calculation schema for depth kernel descriptor generation were proposed. Due to above-mentioned improvements, the category and instance recognition accuracy of the proposed algorithm for objects can respectively reach 86.8% and 92.7%, which are higher than those of the previously hand-crafted feature methods for object recognition from RGB-D images.

In view of the problems of traditional prepaid meters such as inefficiency and inconvenience, a Near Field Communication (NFC)-enabled water meter system that has the functions of mobile payment and data query was proposed. Firstly, according to the business requirements of the prepaid water meter, the overall architecture of the water meter system was developed based on NFC technology, and the software and hardware were designed. Secondly, a low-power mechanism which was used to wake up the water meter by detecting the external magnetic field changes was proposed. Finally, the security performance in mobile payment of the water meter system was analyzed based on NFC security protocols. The experimental results show that users can dynamically awake the water meter system, and utilize the functions of mobile payment, data querying and data uploading, by using the NFC mobile phones or other mobile terminals with NFC module.

Aiming at the problem that the existing robust cascade postural regression algorithm lacks shape constraint, and has low localization accuracy and unsatisfactory success rate in complex face and occlusion situations, a novel positioning algorithm for pose estimation of facial feature points was proposed to improve the accuracy and success rate. A regional block operation was performed on face feature points to implement shape constraint. To improve the algorithm performance, a regression operation was performed on partial feature point positions to reduce the scale of regression, and the shape index feature was introduced to sampling prior operation. The experimental results show that the proposed algorithm has higher localization accuracy and robustness for complex face and occlusion, and meets the realtime requirement.