Technology and method for guaranteeing authenticity of network learning by applying face recognition

Abstract

The invention discloses a technology and a method for guaranteeing the authenticity of network learning by applying face recognition, relates to the field of artificial intelligence and Internet, and provides a technology and a method for guaranteeing the authenticity of network learning by applying face recognition. Learning monitoring in learning: monitoring by selecting one or more of a real-time verification monitoring strategy, a timing verification monitoring strategy, a random verification monitoring strategy and a secret document learning monitoring strategy, and automatically producing a learning record file: combining data collected in a learning process according to rules to generate the learning record file. According to the invention, the face recognition verification technology is applied before learning, during learning and after learning, so that the phenomena of learning by others, idle learning hour and the like are effectively prevented; the confidential document learning monitoring strategy can solve the confidentiality problem of learning important and secret files in a network mode; in addition, by automatically generating an electronic edition learning record file, later manual comparison and inspection are also facilitated.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and the Internet, in particular to a technology and method for applying face recognition to ensure the authenticity of network learning. Background technique [0002] Face recognition technology is a relatively mature human biometric identification technology, which has been widely used in human body identity verification in the fields of payment, access control, monitoring, etc. In the present invention, this technology is effectively combined with network learning, which is a Techniques for monitoring student learning in a remote network environment. The existing prior art closest to the present invention at present: Method 1: Send a dynamic verification code to the student's mobile phone, and the student can start or continue learning only after the student has correctly entered the received verification code. Method 2: The dynamic code verification window for letters, numbers, and pict...

AI Technical Summary

Problems solved by technology

[0003] However, the above-mentioned prior art has the following defects: 1. There is a lack of monitoring means for students to personally participate in learning under the network environment. Students only need to inform others of their account numbers and passwords, or give their mobile phones to others, and then others can complete online learning instead of students. , the authenticity of learning behavior cannot be guaranteed, and the phenomenon of acting as a substitute cannot be fundamentally eliminated, and it is difficult to achieve the expected purpose and effect of using the network to carry out learning; Confidentiality of important and secret documents; 3. Record files cannot be automatically generated, which is not conducive to manual comparison and inspection in the later stage

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