Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a linear representation based face illumination normalization method is put forward. According to the Lambertian re- flectance model, the specific illumination of a face image is determined linearly by a three-dimensional light source orientation vector. Based on the energy function proposed by the Quotient Image approach,  pro- pose a two-step iterative optimization strategy to work out this three- dimensional face illumination representation coefficient vector. Then the neural illumination description coefficients learned from training set are transferred into each face image captured under arbitrary lighting condi- tion to uniform face illumination. Our approach could effectively reduce the disturbance of varying illumination to recognition accuracy. The ef- fectiveness of the proposed method is evaluated on the Extended Yale B database.
['Yuelong Li', 'Li Meng', 'Jufu Feng']
I am writing a research paper on a idea Lighting Coefficients Transfer Based Face Illumination Normalization
[{"from": "human", "value": "I am writing a research paper on a idea Lighting Coefficients Transfer Based Face Illumination Normalization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a linear representation based face illumination normalization method is put forward. According to the Lambertian re- flectance model, the specific illumination of a face image is determined linearly by a three-dimensional light source orientation vector. Based on the energy function proposed by the Quotient Image approach,  pro- pose a two-step iterative optimization strategy to work out this three- dimensional face illumination representation coefficient vector. Then the neural illumination description coefficients learned from training set are transferred into each face image captured under arbitrary lighting condi- tion to uniform face illumination. Our approach could effectively reduce the disturbance of varying illumination to recognition accuracy. The ef- fectiveness of the proposed method is evaluated on the Extended Yale B database."}]