In order to solve the problem that the accuracy of face recognition is not good due to the influence of jitter and environmental factors in the mobile shooting environment, this paper proposes the application of an artificial neural network algorithm in the information scanning and recognition of face biological images. The spatial neighborhood information is integrated into the amplitude detection of multipose face images, and the dynamic corner features of multipose face images are extracted. The structural texture information of multipose face images is compared to a global moving RGB three-dimensional bit plane random field, and the multipose face images are detected and fused. At different scales, appropriate feature registration functions are selected to describe the feature points of multipose face images. The parallax analysis of target pixels and key feature detection of multipose face images are carried out. Image stabilization and automatic recognition are realized by combining artificial neural network learning and feature registration methods. The experimental results show that the experiment is designed with MATLAB, the frame frequency of dynamic face image acquisition is 1200 kHz, the number of collected samples of the multipose face image is 2000, the training sample set is 200, the noise coefficient = 0.24, the number of multipose face image blocks is 120, and the structural similarity is 0.12. It is found that the output signal-to-noise ratio of multipose face image recognition using the method in this paper is high. Conclusion. This method has good performance in feature point registration and high recognition accuracy for multipose face image recognition.

SummaryThree‐dimensional (3D) audio reproduction technique is a hot topic since MPEG proposed a proposal for 3D audio standard in 2012. The present 3D reproduction method is not available in vehicle because the loudspeaker configuration does not meet the requirements of the methods. In this paper, we develop a regression model to reproduce sound pressure in vehicle using a data mining algorithm. A big sound pressure dataset is established based on acoustic theory. We analyze this dataset and propose a stepwise regression model training and testing on this dataset. Both the Mean Square Error and Root Mean Square Error are rather small, and all the residuals such as raw residuals, Pearson Residuals, Studentized residuals, and Standardize residuals are small as well. The experiments indicate that the proposed reproduction system accurately simulates the theoretical sound reproduction system.

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