Facial Age Estimation with Deep Convolutional Neural Network Architecture

Facial age estimation is a complex and essential task with applications in biometrics, healthcare, and personalized services. This study explores the use of pre-trained deep convolutional neural network (CNN) architectures, including Xception, Inception, MobileNet, ResNet, and Inception ResNet, to predict age from facial images. These models leverage their hierarchical feature extraction capabilities to capture age-relevant patterns accurately. The evaluation was conducted using key regression metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the tested models, Xception demonstrated the best performance, achieving a MAE of 2.95, MSE of 48.59, and RMSE of 6.97, making it the most accurate and reliable architecture in this study. The findings underscore the effectiveness of pre-trained CNNs in handling the complexities of facial age estimation and emphasize the importance of selecting an appropriate model architecture and optimization techniques. While Xception showed strong accuracy, challenges such as overfitting and dataset bias were partially mitigated through fine-tuning. Future research could focus on incorporating multi-modal data and optimizing these models for deployment on resource-constrained devices. This study provides a robust foundation for advancing facial age estimation systems in both research and practical applications.