Human Age Estimation from Images in Real-Time Application Using Machine Learning and Deep Learning Models

The objective of the project is to build an accurate age estimation system applying machine learning (ML) and deep learning (DL) techniques. Due to its numerous applications in areas including biometrics, recognition of faces, age-based promotion, and forensic investigations, accurately calculating human age from photos of faces has attracted a lot of attention. To understand the complex correlations between face features and age, the model will be trained on a broad dataset of facial images with associated age labels. Data gathering and exploratory data analysis are the first steps in the project's phased strategy, which aims to comprehend the dataset's features and spot any biases or outliers. The utilization of feature engineering approaches, such as landmark detection, texture analysis, and geometric features, will be utilized to extract pertinent and discriminative characteristics from the facial photos. The machine learning (ML) and deep learning (DL) algorithms will use these constructed attributes as input. Training multiple machine learning (ML) and deep learning (DL) models are being carried out. Accuracy is being put to use for evaluating the models' performance. To evaluate the advancements in age estimation accuracy, the results had been compared to previous research. The Gradio platform been applied to incorporate the models into a user-friendly interface which permits to enter facial image data and get results for real-time age estimate. © 2023 IEEE.