Automated face recognition plays a vital role in forensics. The most important evidence in the criminal investigation is the facial images captured from the crime scene, as they represent the identity of the people involved in crime. The role of law enforcement agencies is to identify the facial images from the suitable database. This information can be treated as strong evidence for the law enforcement agencies which becomes the most important evidence in global counter-terrorism initiatives. Contour of chin and cheek, distancebetween different features and shapes of facial components are some of the parameters considered by the forensic experts for manual facial identification process. This process is time consuming, and it is a tedious job. To address this issue, there is a need for developing an automated face recognition system for forensics. As a result, FaceNet – a framework for age variation facial digital images is discussed in this research work. Experiments are evaluated on CSA dataset with three age variations which provides a recognition accuracy of86.8% and performs better than the existing algorithms.