Field of the Invention
[0001] The present invention is related to the Convolutional Neural
Network of Computer science field.

Description:[0002] Nowdays, everyone is hyperactive on social media including children. So it is important to keep the children away from this. So that we can prevent children from having increased anxiety and depression.
[0003] Social media applications are at the forefront of technological innovation and creative expression. Working for a socially-focused platform gives you the opportunity to come up with new ideas, design features that promote positive social interaction, and explore innovative ways to address social challenges. You can combine your passion for technology and social impact to create a platform that truly makes a difference.
[0004] The background of the above patent is rooted in the increasing demand for robust and accurate user age detection systems in various industries and applications. Traditionally, age estimation methods relied on simplistic approaches that often lacked precision and reliability. With advancements in machine learning and computer vision techniques, researchers and developers have been able to explore more sophisticated
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and data-driven approaches to tackle this challenge.
[0005] The background research for this patent involved a comprehensive analysis of existing age recognition technologies, including facial recognition algorithms, voice analysis techniques, and multimodal data fusion methods. Extensive datasets containing labeled age information were utilized to train and refine the machine learning models. The inventors conducted experiments, exploring different feature extraction techniques, network architectures, and training strategies to optimize the accuracy and efficiency of the age detection system.
[0006] The aim of this background research was to overcome the limitations of conventional age estimation approaches and develop an advanced system that could accurately and reliably determine the age of users in real-world scenarios. By leveraging the power of machine learning, the inventors sought to create a solution that could revolutionize age recognition technology, paving the way for more personalized and tailored experiences in various domains, from targeted advertising to age-restricted content management.
[0007] The aforementioned patent's goal of innovation is to create a sophisticated age detection system that makes use of machine learning algorithms to precisely determine the age of users. The main goal is to deliver a more reliable and accurate answer by overcoming the constraints of the current age estimation techniques. The innovation seeks to increase the age recognition's accuracy and dependability
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through the use of machine learning techniques, enabling more effective personalization and customised experiences across a range of applications.
[0008] The creation of a flexible age identification system that can assess several modalities, including facial features, vocal traits, and maybe other pertinent indicators, is another important goal. This multidimensional strategy offers a more thorough grasp of user age, improving the system's precision. The idea aims to develop a solution that can adapt and improve over time, continuously learning from fresh data and adapting to accommodate varied user groups by utilising the power of machine learning.
[0009] A scalable and effective age d9etection system that can be smoothly included into a variety of platforms and devices is another goal of innovation. The development intends to retain high accuracy while optimising computational resources, making it appropriate for implementation in real-time applications like digital signs, access control systems, or personalised content distribution. The objective is to provide a solution that is easy to use, affordable, and able to handle massive amounts of data, enabling broad adoption and useful deployment across diverse industries.
, Claims:The system and method is a smart method to detect the age of user by the captured image.
2. The system in claim 1, claims the following:
a. No external device is used
b. Free of cost operation
c. High accuracy
d. Easy accessibility
3. The system of claim 1 . The system can easily and automatically uses the deep learning
method for learning through CNN algorithm