A novel facial age estimation through super-resolution based on deep learning

Abstract

Abstract Age estimation encounters challenges due to the low resolution (LR) of images captured in real-world scenarios, emphasizing the critical role of image quality in achieving accurate age estimations. Despite efforts to enhance age estimation through LR image training, existing methodologies often overlook the incorporation of super-resolution (SR) as a crucial pre-processing step for LR images. This study introduces an innovative approach that integrates SR of facial images with age estimation methodologies. Specifically, a hierarchical reconstruction network (HRN) structure is employed to train LR images, fully integrating shallow concrete features and deep abstract features. Validation of the training outcomes includes evaluation metrics such as peak signal-to-noise ratio and structural similarity index measurement, showing superior performance compared to several state-of-the-art approaches. Subsequently, an enhanced soft stage regression network is designed for facial age measurement and trained using the SR-IMDB dataset generated by HRN to minimize mean absolute error. This approach achieves a final result of 7.33, indicating an average performance improvement of 10.94% compared to five other state-of-the-art methods. Experimental results highlight the effectiveness of integrating SR into the age estimation process.