Along with internet growth, security issues come into existence. Efficient tools to control access and to filter undesirable web content are needed all the time. In this paper, a control access method for web security based on age estimation is proposed, where the correlation between human age and auditory perception is taken into account. In particular, access is denied if a person’s age is not appropriate for the given web content. Unlike existing web access filters, our biometric approach offers greater security and protection to individual privacy. From a technical point of view, the machine-learning regression model is used to estimate the person’s age. The primary contributions of this paper include an age estimation module based on human auditory perception and provision of an open-source web filter to prevent adults from accessing children web applications. The proposed system can also be used to limit the access of children to a webpage specially designed for adults. Our system is evaluated with a dataset collected from 201 persons with different ages from 06 to 60 years old, where it considered 109 male and 82 female volunteers. Results indicate that our system can estimate the age of a person with an accuracy of 97.04% and a root mean square error (RMSE) of 4.2 years. It presents significant performances in the verification scenario with an Equal Error Rate (EER) of 1.4%.