Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long‐distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB‐FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross‐entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA‐FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively.

Aiming at the photo fraud that often occurs in identity verification and the accuracy and robustness of real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. This method first extracts the image texture features through the LBP algorithm, which eliminates the problem of illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from different angles or at different times to obtain more facial details. In addition, the fusion of local and global features is realized by SPP pooling, which enriches the expression ability of feature maps and improves detection accuracy. Finally, the eye EAR value is calculated by the face key point detection technology to achieve face anti-spoofing, and then the real-time face recognition against fraud is realized. The experimental results show that the algorithm proposed in this paper has good accuracy on NUAA, CASIA-SURF and CASIA-FASD datasets, which can reach 99.48%, 98.65% and 99.17%, respectively.

Expressions serve as intuitive reflections of a person’s psychological state, making the extraction of effective features for accurate facial expression recognition a crucial research problem. However, when facial information is incomplete, the existing convolutional neural networks face some challenges in extracting features. To address this issue, this paper introduces a pyramidal convolutional attention residual network(PCARNet) based on the ResNet18. PCARNet combines the pyramidal convolution module and an improved convolutional attention mechanism to effectively extract expression features and achieve high-precision facial expression recognition. The proposed model utilizes pyramidal convolution to extract facial expression features at multiple scales, capturing both global and local information of the face. Grouped convolution is employed to reduce the computational complexity and the number of parameters. Additionally, to avoid the adverse effects of channel dimensionality reduction on the attention mechanism and enhance the capacity for information exchange across channels, the Share MLP module within the convolutional attention mechanismwas replaced by a one-dimensional convolution with adaptive kernel size. The improved convolutional attention mechanism assigns weights to the extracted multiscale features based on both channel and spatial dimensions, enhancing the representation of crucial facial features. Experimental results demonstrate the high recognition accuracy of the proposed method on public datasets such as Fer2013, RAF-DB, and CK+. The accuracies achieved are 73.725%, 87.516%, and 95.455%, respectively. Compared to other methods, the proposed approach shows improvements of at least 1.4%, 2.4%, and 0.25% on the respective datasets, confirming its high reliability and performance.

Accurate face recognition technology is of great significance for face anti-counterfeiting. Due to illumination, posture, angle, and other reasons, the existing face liveness detection technology is difficult to adapt the environmental changes, resulting in low detection accuracy. To address this issue, this paper presents a novel high-performance face anti-spoofing detection method named RGCS_ConvNeXt. The data-enhanced face images are fed into the ConvNext network, which group convolution is added to extract the correlation between different features, and the coordinate attention mechanism is used to enhance the facial feature extraction capability both spatially and directionally. Then SPPF is used to extract the features at different scales to enhance the representation of the feature map. Finally, the facial key point detection technique is utilized to calculate the eye EAR value to achieve accurate face anti-counterfeiting recognition. The proposed algorithm shows an average classification error rate of 0.3%, 1.7%, 1.9%±1.5% and 2.8%±3.4%, respectively, on the four protocols of the OULU-NPU public dataset. On the Siw dataset, the average classification error rate is 0.69%, a reduction of 0.02% compared to the MA-Net network. The half-error rate on the MSU-MFSD dataset is 2.39%, a 0.21% reduction compared to the DPCNN network. The algorithm shows good accuracy on the OULU-NPU, MSU-MFSD and Siw datasets, reaching 99.64%, 98.40% and 99.25% respectively, 0.26% higher than the SE-FeatherNet network’s average accuracy.

In the community of Granular Computing, knowledge is interpreted as one classification ability of realistic or abstract objects. Generally, the concept of granularity is used for characterizing such an ability, which has been widely explored in literatures. To calculate the parameterized granularity, a naive approach is to find the granularity in terms of the parameter one by one. Nevertheless, such approach can only generate the single parameter based knowledge granularity, and the difference of knowledge granularities among different parameters may be slight. It follows that the knowledge granularity derived from single parameter may be lack of representativeness. In this paper, the continuous parameters based knowledge granularity is proposed, and the corresponding calculation approach is presented. Inspired by the thinking of definite integral in mathematical problems, the calculation approach is mainly implemented by following steps: firstly, the graph formed by granularity and parameter interval is divided into several small rectangles whose length of interval tends to be 0; secondly, the sum of area values of all the small rectangles is calculated; finally, the obtained area value divided by the whole length of parameter interval can be considered as the continuous parameters based knowledge granularity. This study suggests a new trend of handling problems related to knowledge from the viewpoint of continuity.