Deep Learning for age Estimation

The significance of age recognition from faces in various domains, including minor security and online shopping, has drawn potential attention. It still faces a number of difficulties, though, particularly in an unrestricted setting, including anti-aging therapies, lighting circumstances, and filter effects. Advancements in computer vision offer promising avenues for more precise age prediction, particularly in facial analysis. However, challenges persist, including the need for robust datasets, standardized protocols, especially in computational approaches and generalization model's. This work provides an overview of the three methodologies employed in age estimation. The performance of their approach is evaluated by confusion matrix. The first test, which uses three datasets separately, can demonstrate that using just one dataset is insufficient to produce a high-quality confusion matrix. The second test overcome this problem by combine data from various sources, apply data augmentation technique and perform early stopping to achieve an excellent confusion matrix for age recognition. Obtained results validate the proposed approach.