Experimental Evaluation of IoT based Human Gender Classification and Record Management using Intelligent Hybrid Learning Principles

There has been a lot of recent interest in automatically predicting age and gender from facial images because to the several uses this has in different kinds of facial investigations. Using the aforementioned technology, we can tell a person’s age and gender from only one image. In this research, we develop a unique deep learning approach, Intelligent Hybrid Learning Principles (IHLP), and apply it to the task of classifying facial images according to gender. Convolutional Neural Networks (CNNs), a standard learning technique, serve as the inspiration for the suggested logic. Both models were tested in experiments designed to see how well they could distinguish between male and female faces. To approximately estimate a human face’s gender and age from a picture, the primary goal of this research is to apply a deep learning based IHLP to construct a gender and age detector. In order to accomplish a gender classification/identification job, automatic face recognition seeks to extract the important bits of information from humans and combine them into a usable representation. All of them, however, have something holding them back, such as inaccurate or incomplete reflection of the face’s structure or texture. Both alignment of faces and identification of faces may benefit greatly from this method. We propose a straightforward convolutional neural network design based on Intelligent Hybrid Learning Principles for this purpose, which can be used even in the face of a scarcity of training data. The effectiveness of the suggested method is shown by cross-validation using a model of a convolutional neural network, which then serves as evidence for the resultant section. Apart from this an advanced logic of Internet of Things (IoT) is adopted over this approach to maintain the records clearly for further analysis and it is easy to recover the records for further use.