Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. ,  develop a method for recognizing 3D face images by combining face component, Fisherface method, and fuzzy integral. For the proposed approach, the first step uses face curvatures which present the facial features for 3D face images, after normalization using the SVD. As a result of this process,  obtain curvature feature for each component range face. PCA and Fisherface method are then applied to each component range face. The reason for adapting PCA and Fisherface method is that the methods maintain the surface attribute for face curvature, even though they can generate reduced image dimension. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each component. The experimental results show that the proposed approach has outstanding classification in comparing with other methods.
['Yeunghak Lee', 'David Marshall']
I am writing a research paper on a idea Curvature based normalized 3D component facial image recognition using fuzzy integral
[{"from": "human", "value": "I am writing a research paper on a idea Curvature based normalized 3D component facial image recognition using fuzzy integral"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. ,  develop a method for recognizing 3D face images by combining face component, Fisherface method, and fuzzy integral. For the proposed approach, the first step uses face curvatures which present the facial features for 3D face images, after normalization using the SVD. As a result of this process,  obtain curvature feature for each component range face. PCA and Fisherface method are then applied to each component range face. The reason for adapting PCA and Fisherface method is that the methods maintain the surface attribute for face curvature, even though they can generate reduced image dimension. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each component. The experimental results show that the proposed approach has outstanding classification in comparing with other methods."}]