Modeling the human face's aging process is crucial for cross-age face verification and recognition, garnering increasing attention due to its diverse applications in areas such as cross- age recognition and entertainment. Potential uses include aiding in the identification of lost children or predicting future appearances. However, the scarcity of labeled facial data spanning long age ranges presents a significant challenge. Additionally, due to varying aging rates among individuals, many existing models focus on synthesizing faces within broader age groups rather than predicting a specific age. Most methods primarily highlight prominent changes, like wrinkles and facial shape, which often fail to preserve individual identity. This research proposes a novel approach that preserves facial identity while simulating aging using Conditional Generative Adversarial Networks. By employing this technique, more realistic and identity- consistent facial images are generated. The model is further evaluated using identity preservation metrics and age classification, supported by user studies on face verification and age estimation.
