A Deep CNN technique for Age Estimations from Facial Images

Abstract

Facial age estimation plays a vital role in various applications within human-computer interaction, such as personalized user experiences, age-specific content filtering, and demographic studies. The process involves analyzing facial images to predict a person’s age or categorize them into an age group, and it requires sophisticated methodologies to achieve high accuracy. One of the main challenges in this domain is the need for extensive datasets and long training times to develop accurate predictive models. In this paper, we propose a novel deep learning-based age estimation model using Convolutional Neural Networks (CNN). Our approach is designed to predict ages with high precision, addressing both regression and classification problems in age estimation. Notably, the proposed model demonstrates superior performance with a significantly reduced training data requirement, achieving a low Mean Absolute Error (MAE) of 2.1 years and an impressive accuracy rate of 93%. These results underscore the model's ability to generalize effectively across varying datasets. We implemented and evaluated two custom CNN models tailored for facial age prediction. The first model approaches age estimation as a regression problem, directly predicting the numerical age, while the second model frames it as a classification task, categorizing images into predefined age groups. The performance of our system was benchmarked against existing state-of-the-art methods, and the results highlight its enhanced efficiency and accuracy. Our findings emphasize the practicality and reliability of the proposed method, paving the way for advancements in applications requiring age estimation. This work contributes to reducing computational overhead and improving accuracy, making it an essential step forward in human-computer interaction systems.