Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map,  compute their 3D coordinates.  next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view.  increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.
['A-Nasser Ansari', 'Mohamed Abdel-Mottaleb', 'Mohammad H. Mahoor']
I am writing a research paper on a idea Disparity-Based 3D Face Modeling using 3D Deformable Facial Mask for 3D Face Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Disparity-Based 3D Face Modeling using 3D Deformable Facial Mask for 3D Face Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map,  compute their 3D coordinates.  next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view.  increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate."}]