REGRESSION AND CLASSIFICATION MODELS FOR HUMAN AGE PREDICTION

Abstract

This study aims to enhance automated age prediction from facial images, a task with significant potential in security, law enforcement, and Human-Computer Interaction (HCI). While age estimation has seen progress, it remains a challenging problem due to the diverse factors influencing facial aging, such as genetics, environment, lifestyle, and facial expressions. These variations result in individuals of the same chronological age looking markedly different. Most existing age estimation methods rely on computationally intensive pre-trained models, often treated as "black boxes" with predefined input sizes and limited flexibility. To address these limitations, we propose using Convolutional Neural Networks (CNNs) for age prediction. Our approach combines classification and regression techniques to predict age more accurately. We applied our model to publicly available datasets, including FGNET, Adience, APPA-REAL, UTKFace, and All-Age-Face, encompassing images from constrained and unconstrained environments. The proposed CNN model was evaluated against existing pre-trained models, demonstrating comparable performance in age prediction tasks. Both classification and regression results underscored the model's accuracy, offering additional benefits in reduced computational complexity, increased flexibility, and adaptability. This study introduces a CNN-based approach as a viable alternative to pre-trained models for automated age prediction. It offers competitive accuracy while addressing critical limitations of current models, such as computational demands and lack of flexibility, thus contributing a more efficient solution for age estimation tasks in various real-world applications.