[Retracted] Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor

Abstract

With the continuous innovation of network technology, various kinds of convenient network technologies have grown, and human dependence on network technology has gradually increased, which has resulted in the importance of network information security issues. With the continuous development of my country’s industrialization, the application of sensors is becoming more and more extensive, for example, the security vulnerabilities and defects in the operating system itself. Traditional sensors can “perceive” a certain thing or signal, convert it into an electrical signal and record it, and then use a conversion circuit to output the electrical signal into a value or other display form that is conducive to observation. Nowadays, sensors have been further developed. Based on the original “perception” function, combined with computer technology, it integrates data storage, data processing, data communication, and other functions, so that it has analysis functions and can better display information. The technical level has reached a new level. Early intelligent recognition mainly used the uniqueness of finger and palm lines to scan and contrast, but due to some weather reasons or skin texture constraints caused by skin texture, these methods showed certain limitations. This paper proposes a new computer vision-based algorithm from face detection technology and face recognition technology. In the face detection technology, it is mainly introduced from the OpenCV method. Face recognition technology is improved in practical applications through the Seetaface method and YouTu method. At the same time, using the contrast experiment, the detection and recognition rates under the three different requirements of side face detection, occlusion detection, and facial exaggerated expression are compared, and the accuracy of each method is improved. The results show that each case is compared in each case. The advantages and disadvantages of the algorithm effectively verify the effectiveness of the method.

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