In the world of biometric authentication, 3D face recognition stands as a pivotal era. That’s why, this paper presents a groundbreaking method for building and processing 3D face structures that may later be used for superior and multidimensional face recognition. The method obviated the need for complicated scanners and heavy-powered graphic processing gadgets. The proposed approach uses a gaggle of 2D RGB images extracted from half of a minute video covering the frontal half of the face from one ear to another. Further, it uses the concept of structure from motion and photogrammetry, which enable the extraction of facial features in the form of point clouds. These extracted face structures are then processed, the usage of the iterative closest point method for shape aligning coupled with density-based spatial clustering of applications with noise for getting rid of noisy factors. This research not only signifies a significant leap in the domain of 3D face recognition but also has far-achieving implications. The ramifications of these structures enlarge past mere technological innovation, and it engenders realistic integration opportunities in ordinary eventualities, ranging from mobile devices to surveillance cameras. Consequently, the research contributes to organizing a more secure and extra secure digital panorama, improving the resilience of authentication systems and reinforcing the rules of virtual agreement.