Abstract: A computerized method useful for rendering a 3D image for automated ear morphing comprising: obtaining a face image, wherein the face image comprises a digital image of a view of a user's ears; detecting whether an ear in the digital image of a view of a user's ears is covered or not covered with a classifier; annotating a data side images of the digital image of the view of a user's ears; building a model to detect the ear beginning with a plurality of plurality of 2D landmarks of the user in the digital image; transferring the plurality of 2D landmarks of the user to a 3D model of the user; using a FLAME functionality to fit on the ear to the 3D model of the user; and morphing the ears into a morphed digital image.

Abstract: In one aspect, a computerized method for rendering a three-dimensional (3D) digital image for automated facial morphing includes the step of determining that a user's face is in a compliant state. The method includes the step of scanning of the user's face with a digital camera to obtain a set of digital images of the user's face. The method includes the step of implementing an analysis of the set of digital images and implementing a set of pre-rendering steps. Each digital image comprises a depth data, a red/green/blue (RGB) data, and a facemask data, implementing an iterative closest path (ICP) algorithm that correlates the set of digital images together by stitching together the cloud of points of the facemask data of each digital image and outputs a set of transformation matrices.

Abstract: In one aspect, a computerized method for implementing a multi-pass iterative closest point (ICP) registration in an automated facial reconstruction process includes the step of providing an automated facial reconstruction process. The method includes the step of detecting that a face tracking application programming interface (API) misalignment has occurred during the automated facial reconstruction process. The method includes the step of implementing a multi-pass ICP process on the face tracking API misalignment by the following steps. One step includes obtaining a first point cloud (PC1) of the automated facial reconstruction process. The method includes the step of obtaining a second point cloud (PC2) of the automated facial reconstruction process. The method includes the step of obtaining a first face mask (mask1) of the automated facial reconstruction process. The method includes the step of obtaining a second face mask (mask2) of the automated facial reconstruction process.

Abstract: In one aspect, a computerized method for rendering a three-dimensional (3D) digital image for automated facial morphing includes the step scanning of the user's face with a digital camera to obtain a set of digital images of the user's face. The method includes the step of determining that a user's face is in a compliant state. The method includes the step of implementing an analysis of the set of digital images and implementing a set of pre-rendering steps. Each digital image comprises a depth data, a red/green/blue (RGB) data, and a facemask data. The method then implements an iterative closest path (ICP) algorithm that correlates the set of digital images together by stitching together the cloud of points of the facemask data of each digital image and outputs a set of transformation matrices. The method includes the step of implementing a truncated signed distance function (TSDF) algorithm on the set of transformation matrices.

Abstract: In one aspect, a computerized method for implementing a multi-pass iterative closest point (ICP) registration in an automated facial reconstruction process includes the step of providing an automated facial reconstruction process. The method includes the step of detecting that a face tracking application programming interface (API) misalignment has occurred during the automated facial reconstruction process. The method includes the step of implementing a multi-pass ICP process on the face tracking API misalignment by the following steps. One step includes obtaining a first point cloud (PC1) of the automated facial reconstruction process. The method includes the step of obtaining a second point cloud (PC2) of the automated facial reconstruction process. The method includes the step of obtaining a first face mask (mask1) of the automated facial reconstruction process. The method includes the step of obtaining a second face mask (mask2) of the automated facial reconstruction process.

Abstract: In one aspect, a computerized method for rendering a three-dimensional (3D) digital image for automated facial morphing includes the step scanning of the user's face with a digital camera to obtain a set of digital images of the user's face. The method includes the step of determining that a user's face is in a compliant state. The method includes the step of implementing an analysis of the set of digital images and implementing a set of pre-rendering steps. Each digital image comprises a depth data, a red/green/blue (RGB) data, and a facemask data. The method then implements an iterative closest path (ICP) algorithm that correlates the set of digital images together by stitching together the cloud of points of the facemask data of each digital image and outputs a set of transformation matrices. The method includes the step of implementing a truncated signed distance function (TSDF) algorithm on the set of transformation matrices.

Abstract: In one aspect, a computerized method for rendering a three-dimensional (3D) digital image for automated facial morphing includes the step of determining that a user's face is in a compliant state. The method includes the step of scanning of the user's face with a digital camera to obtain a set of digital images of the user's face. The method includes the step of implementing an analysis of the set of digital images and implementing a set of pre-rendering steps. Each digital image comprises a depth data, a red/green/blue (RGB) data, and a facemask data, implementing an iterative closest path (ICP) algorithm that correlates the set of digital images together by stitching together the cloud of points of the facemask data of each digital image and outputs a set of transformation matrices.

Abstract: In one aspect, a computerized method useful for automated facial morphing for eyebrow hair and face color detection includes the step of obtaining a face image. The face image comprises a digital image of a frontal view of a user's face. The method includes the step of identifying a pair of eyebrows in the face image. The method includes the step of generating a brow-less image of the face image by removing the pair of eyebrows from the face image. The method includes the step of, with a digital image of the removed pair of eyebrows, detecting the eyebrow color. The method includes the step of creating a pixel table for an eyebrow candidate region. The method includes the step of defining the eyebrow candidate region as a set of pixels with a different color between the eyebrow color and a brow-less image color.

Abstract: In one aspect, a computerized method useful for automated facial morphing for eyebrow hair and face color detection includes the step of obtaining a face image. The face image comprises a digital image of a frontal view of a user's face. The method includes the step of identifying a pair of eyebrows in the face image. The method includes the step of generating a brow-less image of the face image by removing the pair of eyebrows from the face image. The method includes the step of, with a digital image of the removed pair of eyebrows, detecting the eyebrow color. The method includes the step of creating a pixel table for an eyebrow candidate region. The method includes the step of defining the eyebrow candidate region as a set of pixels with a different color between the eyebrow color and a brow-less image color.