Face Age Prediction Based on Machine Learning

Abstract

Age recognition, an essential attribute of human identity, plays a pivotal role in various social and medical applications. Due to the influences of photography equipment, lighting, and angles, age recognition based on facial images has always been a challenging topic. Fortunately, with the development of machine learning, especially the breakthroughs achieved by neural network models in image-related fields, researchers have applied machine learning to age recognition. From the perspective of research methodologies, age prediction can be regarded as either a regression or classification task. Therefore, this article analyzes from both regression and classification perspectives. Firstly, it introduces the commonly used datasets in the field of facial recognition. Then, it discusses the commonly used regression models, classification models, and optimization methods in age recognition tasks. Finally, the article summarizes the entire text and proposes future research ideas, providing references for researchers in related fields.