Age Based Content Controlling System Using AI for Children - Sangeetha, T; Mythili, K; P, Prakasham; S, Ragul Balaji (EAI Endorsed Transactions on Internet of Things, 2024)
Age detection has gotten a lot of attention in recent years because it is being used in more and more sectors. Regulations and norms imposed by the government, security measures, interactions between humans and computers, etc. Facial features and fingerprints are two of the most common human characteristics that may shift or alter throughout time. The nose, on the other hand, maintains a consistent structure that does not alter with the passage of time and possesses the singular capacity to fulfil the prerequisites of biometric attributes. This study gives a comprehensive review of how deep learning algorithms may be used to easily extract aspects of the human nose. In specifically, convolutional neural networks, also known as CNNs, are utilised for the purpose of feature extraction and classification when applied to big datasets that have numerous layers. The proposed methodology collects more private children's datasets, which contributes to a rise in the total number of datasets, which ultimately results in a rise in the 98.83 percent accuracy achieved. The results of this survey may be used to limit the material that is shared on social media by determining the age range of the participants, from under 18 to 18 and older.
Keywords: Deep Learning; Feature Extraction; CNN; Smart Age Detection; Children