Age Estimation from Facial Images Using the YOLO Algorithm

A computer vision task called “age estimation based on facial images” seeks to estimate a person's age by examining their facial features. It has drawn a lot of interest because of its possible applications in numerous industries. In this research work, a novel approach based on YOLO Algorithm is proposed to solve age estimation. To implement age estimation using YOLO, the algorithm needs to be trained on a dataset that contains annotated images with corresponding age labels. During training, the YOLO network learns to detect facial features and extract relevant information related to age, such as wrinkles, skin texture, and facial expressions. Once trained, the YOLO algorithm can be used to estimate the age of individuals in new images. It analyzes the image and applies regression techniques to predict the age range of each detected face. The output can be a set of predefined age groups or a continuous numerical value representing the estimated age. The trained model is then tested against a validation dataset, and measures like mean absolute error and mean squared error are used to gauge how well it performed. The model can be used to determine the age of fresh facial photos after it has been validated. Experiments are conducted on the app a-real dataset and results are found to be better than current state of art techniques.