As we all know, there are many ways to express emotions. Among them, facial emotion recognition, which is widely used in human–computer interaction, psychoanalysis of mental patients, multimedia retrieval, and other fields, is still a challenging task. At present, although convolutional neural network has achieved great success in face emotion recognition algorithms, it has a rising space in effective feature extraction and recognition accuracy. According to a large number of literature studies, histogram of oriented gradient (HOG) can effectively extract face features, and ensemble methods can effectively improve the accuracy and robustness of the algorithm. Therefore, this paper proposes a new algorithm, HOG-ESRs, which improves the traditional ensemble methods to the ensembles with shared representations (ESRs) method, effectively reducing the residual generalization error, and then combining HOG features with ESRs. The experimental results on the FER2013 dataset show that the new algorithm can not only effectively extract features and reduce the residual generalization error, but also improve the accuracy and robustness of the algorithm, the purpose of the study being achieved. The application of HOG-ESRs in facial emotion recognition is helpful to solve the symmetry of edge detection and the deficiency of related methods in an outdoor lighting environment.
In the new era, the rapid development of information technology makes the face recognition technology in artificial intelligence also develop rapidly, among which, face expression recognition has become a research hotspot. In recent years, due to deep learning, convolutional neural network and multilayer perceptron and other related algorithms have become the research focus of scholars, so their wide application in the field of facial expression recognition is also the direction of face emotion recognition exploration and research. In addition, computer software is widely used in human daily life, so it is also very important to design and implement an intelligent, real-time and universally applicable UI interface for facial emotion recognition system. Therefore, this paper firstly completed the facial expression recognition model based on the Mini_Xception framework of CNN by training the FER2013 expression database. Secondly, the system UI interface is designed and realized through PyQT5, OpenCV, Keras and other libraries. The final results show that the system is based on the algorithm model, and the UI interface designed and realized can not only recognize the saved pictures, but also recognize the real-time face emotions through the camera, and the overall effect of the system is outstanding.
Aiming at the problem of extracorporeal treatment of prostate diseases, an embedded intelligent prostate extracorporeal nerve pacemaker has been designed using smart chips through the principle of digital signal generator. The treatment effect has been tested through the experiment on 22 related patients in the urology department. Experimental results show that the device can accurately simulate a variety of neural signals and the frequency and amplitude of which are adjustable. The performance of the device can be considered as stable and reliable. In particular, it can generate bioelectrical signals which are similar to the basic electrical rhythm of a normal human prostate, so that the bioelectrical activity of prostate pacemaker can return to normal rhythm. The treatment has achieved good auxiliary treatment effects. The device provides a new solution for the auxiliary treatment of prostate diseases.
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