Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. ,  propose a more practical 3D lighting based forensic method by incorporating a facial landmark based 3D morphable model to efficiently fit the face shape.  also introduce a residual error based algorithm to automatically exclude outliers in lighting estimation. Our proposed method is fully automatic and very efficient compared to previous ones. Also, it does not depend on additional images and has better performance for expressional faces. Experiments on a realistic face dataset with variational lighting conditions indicate the efficacy and superiority of our method.
['Bo Peng', 'Wei Wang', 'Jing Dong', 'Tieniu Tan']
I am writing a research paper on a idea Automatic detection of 3D lighting inconsistencies via a facial landmark based morphable model
[{"from": "human", "value": "I am writing a research paper on a idea Automatic detection of 3D lighting inconsistencies via a facial landmark based morphable model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. ,  propose a more practical 3D lighting based forensic method by incorporating a facial landmark based 3D morphable model to efficiently fit the face shape.  also introduce a residual error based algorithm to automatically exclude outliers in lighting estimation. Our proposed method is fully automatic and very efficient compared to previous ones. Also, it does not depend on additional images and has better performance for expressional faces. Experiments on a realistic face dataset with variational lighting conditions indicate the efficacy and superiority of our method."}]