Facial Recognition is considered one of the foundation problems in computer vision. Although there exists plenty of contributions for fundamental Facial Recognition problems. But, the arduous version i.e. Cross-Age Face Recognition still remains a challenge. Cross-Age Face Recognition recognizes an individual over different age groups and captures age Invariant facial features. Existing approaches rely on inherently complex deep learning architecture or other complex algorithms requiring high space, time and computational resources. In this paper, we present a less complex transfer learning based approach for Cross-Age Face Recognition. The proposed approach is grounded on VGG16 CNN as a backbone and further addition of dense layers fine-tunes the model for required classification. The experimentation is done over the FG-NET dataset to capture age invariant features with multiple hyper parameter settings. The performance of the proposed approach indicates promising results on FGNET dataset with training accuracy of 95.96% and training loss of 0.1321 and serves as a platform for future research towards Cross-Age Face Recognition.