Implementation of Customized Adaptive Algorithms for Visualization of Stereoscopic Face Mask

Computer vision and Image processing are some of the most important technical aspects in the domain of object detection and reconstruction. These concepts provide solutions not only restricted to the engineering domain but also across the various domains. In this article, the proposed framework mainly deals with the scenario of the generation of face masks in 3D view with the aid of selfie images using computer vision and image processing methodologies. The proposed framework consists of two kinds of algorithms that generate face mak with the aid of single and multiple images respectively. Those adaptive algorithms are discussed along with their implementation. The adaptive algorithm utilized for multiple image input names as MIIO3D and the adaptive algorithm for single image input named as SIIO3D implementation for the construction of an efficient 3D face mask. The input images considered are from real-time aspects. Data visualization and their comparisons made with the aid of Tableau.

I. Introduction

Given a single image of an everyday object, a sculpture can recreate its 3D shape, even if the object has never been seen before, Presumably, it is familiar with shapes of similar 3D objects and how they appear in images, which enables the artist to estimate its shape. This might not be the exact shape of the object, but it is often a good enough estimate for many purposes. As technology improved, everyone is more interested in taking selfies, images of architectural buildings and monuments, and these images in a 2dimensional format. Even if we take the images, we are missing certain information on the originality due to missing depth and height within the object in 2D images. If we can make a 3D model it can be useful in many aspects such as the reconstruction of architectural buildings, protecting the old architecture in the form of 3D or 3D images, 3D modeling of biological organs, and entertainment. These examples motivated us to form a novel framework for motion Selfie.