AI-based content filtering system using an age prediction algorithm

Ashutosh Upadhyay, K. S.
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Abstract

Computer vision mainly focuses on the automatic extraction, analysis, and understanding of useful information from a single image or video. On the other hand, authenticity is emerging as one of the primary requirements in today's world by developing a system for computer vision complexity. Generally, two robust techniques such as age estimation and face recognition are required to maintain authenticity. In reality, fraud and scams are getting increased, so here this paper has proposed a new combined model for face recognition and age prediction. Face recognition has been implemented and presented in this paper by using a Deep Neural Network. The authenticity problem can be handled by using either facial recognition or age prediction alone; this study has presented a method that employs both of them together to enhance the system's robustness. So, first, this model detects the person's face, and then it predicts the person's age. If the individual is eligible to view the information or perform a task, their access will be limited; otherwise, their access will be restricted. So it helps to solve two difficulties in this case: the person's identification cannot be faked, and their age is also confirmed by the system. (CNN for the face, and mention technique for the age.)
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使用年龄预测算法的基于人工智能的内容过滤系统
计算机视觉主要侧重于从单个图像或视频中自动提取、分析和理解有用信息。另一方面,通过开发计算机视觉复杂性系统,真实性正在成为当今世界的主要要求之一。通常需要年龄估计和人脸识别两种鲁棒技术来保持图像的真实性。在现实生活中,欺诈和诈骗越来越多,因此本文提出了一种新的人脸识别和年龄预测相结合的模型。本文利用深度神经网络实现了人脸识别。真实性问题可以通过单独使用面部识别或年龄预测来解决;本研究提出了一种将两者结合使用以增强系统鲁棒性的方法。首先,这个模型检测人的脸,然后预测这个人的年龄。如果个人有资格查看信息或执行任务,他们的访问将受到限制;否则,他们的访问将受到限制。因此,它有助于解决这个案例中的两个困难:人的身份不能伪造,他们的年龄也被系统确认。(CNN的脸,提到技术的年龄。)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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