The face recognition system is vulnerable to spoofing attacks by photos or videos of a valid user face. However, edge degradation and texture blurring occur when non-living face images are used to attack the face recognition system. With this in mind, a novel face anti-spoofing method combines the residual network and the channel attention mechanism. In our method, the residual network extracts the texture differences of features between face images. In contrast, the attention mechanism focuses on the differences of shadow and edge features located on nasal and cheek areas between living and non-living face images. It can assign weights to different filter features of the face image and enhance the ability of network extraction and expression of different key features in the nasal and cheek regions, improving detection accuracy. The experiments were performed on the public face anti-spoofing datasets of Replay-Attack and CASIA-FASD. We found the best value of the parameter r suitable for face anti-spoofing research is 16, and the accuracy of the method is 99.98% and 97.75%, respectively. Furthermore, to enhance the robustness of the method to illumination changes, the experiment was also performed on the datasets with light changes and achieved a good result.

Mountain summits are vital topographic feature points, which are essential for understanding landform processes and their impacts on the environment and ecosystem. Traditional summit detection methods operate on handcrafted features extracted from digital elevation model (DEM) data and apply parametric detection algorithms to locate mountain summits. However, these methods may no longer be effective to achieve desirable recognition results in small summits and suffer from the objective criterion lacking problem. Thus, to address these problems, we propose an improved Faster region-convolutional neural network (R-CNN) to accurately detect the mountain summits from DEM data. Based on Faster R-CNN, the improved network adopts a residual convolution block to replace the traditional part and adds a feature pyramid network (FPN) to fuse the features with adjacent layers to better address the mountain summit detection task. The residual convolution is employed to capture the deep correlation between visual and physical morphological features. The FPN is utilized to integrate the location and semantic information in the extracted feature maps to effectively represent the mountain summit area. The experimental results demonstrate that the proposed network could achieve the highest recall and precision without manually designed summit features and accurately identify small summits.

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Aiming at the serious homogenization problems between the products with the same model and batches, and the difficulty identification due to wear, pollution and other factors which led small characteristic differences between cutters failure to identify, an identification feature design method of homogenization product to prevent tracking failure was presented. The feature enhanced direct parts marking (DPM) was analyzed, Data Matrix (DM) codes feature design based on marking process and affine transformation was proposed. Feature differences between individuals were enhanced from the source, DM code texture feature design based on Gray-level Co-occurrence (GLCM) was proposed and DM code texture library was established in product manufacturing process. By using the method of Hamming distance and Euclidean distance, the similarity between the target characteristics and DM code marking feature, manufacturing process texture library was matched when product identification was failed to read. The experimental results showed that the proposed method could identify the product failure recovery or remedy better.