With the widespread use of smart devices such as smartphones, facial recognition applications have experienced rapid growth. Additionally, breakthroughs in deep learning algorithms have led to the development of facial recognition technology based on deep convolutional networks, greatly improving recognition speed and accuracy. Therefore, this study proposes a real-time face recognition method based on the MTCNN-Inception-ResNet-v2-SVM model. In the facial detection phase, the MTCNN algorithm, which offers better overall performance compared to face detection algorithms such as OpenCV and Dlib, is utilized. Data augmentation and other methods are employed in the image preprocessing phase to enhance data diversity. The Inception-ResNet-v2 deep convolutional neural network is used as the backbone network for feature extraction in the facial recognition backbone network section. In the final classification stage, an SVM classifier is employed for the ultimate facial classification. Comparative analyses with models such as Inception-ResNet-v1, Inception-v3, and Inception-v4 are conducted in the backbone network section, determining that the Inception-ResNet-v2 model exhibits the best overall performance. The final result is a real-time face recognition model, MTCNN-Inception-ResNet-v2-SVM, with a high accuracy of 98.79% and fast processing speed.

Face recognition technology has made significant progress in recent years, driven by deep learning and other technologies, and is widely used in public safety, financial payment, intelligent access control, and other fields. Deep learning can effectively extract discriminative features from face images by constructing neural network structures and automatically learning feature mapping relationships, to improve the recognition accuracy. Deep learning shows excellent robustness when dealing with complex scenarios, and scientific metrology and visual analysis tools such as Citespace play an important role in analyzing the current status and development trend of applied research in the field of face recognition. Deep learning methods such as data enhancement techniques and generative adversarial networks have shown strong performance in face recognition tasks. In the future, the further integration and development of deep learning and face recognition technology will promote technological innovation and application expansion. Face recognition technology has important application potential in the digital society and will have wider application prospects in the future.

The condition known as diabetic retinopathy is a severe and common complication of diabetes. It affects the retina, which is a light-sensitive organ inside the eye, and it can lead to blindness or loss of vision. It is therefore important to improve the diagnosis and classification of this disorder. In deep learning, transfer learning is a process that aims to improve the performance of a given task by taking advantage of the knowledge that has been acquired from another. The main idea of this approach is to speed up the learning process by applying the obtained knowledge to a new task. In this paper, with the help of migration learning, a pre-trained deep learning model known as InceptionV3 was used to classify fundus images the Diabetic Retinopathy 2015 Data Colored Resized database in five categories according to the severity of the lesions. It was able to achieve a 92.314% accuracy on a test set.

In order to acquire and fully exploit multispectral information of scenes, and to establish a foundation for the research of multispectral image algorithms, a laboratory has developed a multispectral infrared detector capable of detecting short-wave infrared, mid-wave infrared, and long-wave infrared in three bands. Additionally, a preliminary electronic control system has been designed. The system employs a field-programmable gate array (FPGA) to accomplish the timing control, data acquisition, preprocessing of individual channel images, and multispectral image fusion of the detector. The preprocessing involves black level correction, blind element correction, non-uniformity correction, and histogram equalization of the raw images. For trispectral fusion, color enhancement is applied to linearly combined images in the YUV color space using a color transfer method. The system utilizes a pipeline design to ensure algorithm efficiency. In simulation, the electronic control system for the multispectral infrared detector obtains trispectral information of the scenes, and through image preprocessing and fusion, enhances the understanding of scene information.