Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: At present there are many methods that could deal ll with frontal view face recognition when there is sufficient number of representative training samples. Hover, few of them can work ll when only one training image per class is available. ,  present a method of face recognition based on projection map and singular value decomposition (SVD) to solve the one training sample problem, to acquire more information from the single training sample, training image is linearly combined with its projection map into a new training image, by using Fourier transform, the spectrum representation of face image is obtained that is invariant against spatial translation. Then the spectrum representation is projected into a uniform eigen-space that is obtained from SVD of standard face image and the coefficient matrix is used as feature for recognition. The proposed algorithm obtains acceptable experimental results on the ORL face database
['Jiazhong He', 'Minghui Du']
I am writing a research paper on a idea Face Recognition Based on Projection Map and SVD Method for One Training Image per Person
[{"from": "human", "value": "I am writing a research paper on a idea Face Recognition Based on Projection Map and SVD Method for One Training Image per Person"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: At present there are many methods that could deal ll with frontal view face recognition when there is sufficient number of representative training samples. Hover, few of them can work ll when only one training image per class is available. ,  present a method of face recognition based on projection map and singular value decomposition (SVD) to solve the one training sample problem, to acquire more information from the single training sample, training image is linearly combined with its projection map into a new training image, by using Fourier transform, the spectrum representation of face image is obtained that is invariant against spatial translation. Then the spectrum representation is projected into a uniform eigen-space that is obtained from SVD of standard face image and the coefficient matrix is used as feature for recognition. The proposed algorithm obtains acceptable experimental results on the ORL face database"}]