Deep Learning for Age and Gender Recognition from Facial Images: A Comparative Study of EfficientNet Variants with Explainability

Age and gender estimation from facial images is a critical task in computer vision with wide-ranging applications in human–computer interaction, marketing, surveillance, and social media personalization. While deep learning models have achieved high accuracy, two key challenges persist: (a) finding an optimal balance between computational efficiency and performance across different model scales, and (b) understanding the internal decision-making process of these models. This study presents a comprehensive investigation addressing both challenges through a comparative evaluation of two members of the EfficientNet family, EfficientNet B4 and EfficientNetV2-S, for age and gender classification. We leveraged the Adience and UTKFace benchmarks, employing preprocessing techniques including face detection, alignment, normalization, oversampling, and augmentation to combat overfitting. EfficientNet B4 achieved notable results, including 95.11% accuracy for age classification on UTKFace, establishing a new state of the art for that benchmark. Subsequently, EfficientNetV2-S was evaluated for its improved training speed and parameter efficiency, achieving competitive performance across all tasks. Distinct from standard classification studies, this work further emphasizes model interpretability by integrating Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize and benchmark the specific facial regions influencing the EfficientNetV2-S network’s predictions. The Grad-CAM analysis reveals that the trained model effectively prioritizes relevant facial features rather than relying on background noise, validating that the network learns meaningful representations for age and gender recognition. These findings highlight the efficacy of the EfficientNet family not only as accurate classifiers but also as interpretable models suitable for real-world deployment.