Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gabor feature based classification approaches are widely used in face recognition, because they are insensitive to changes in illumination and facial expression. Hover, most of strategies only use the magnitude of the Gabor wavelet representation of images to generate feature vectors. When only single training image per person is available, the performance of these methods may be limited. , by making use of the slope angle as ll as the magnitude of the Gabor wavelet response,  propose a novel Enriched Gabor feature based Principal Component Analysis (EGPCA) algorithm for face recognition with one training image per person. Experiment results show that the algorithm has better performance than other methods such as (PC)$^{2}$A, E(PC)$^{2}$A and SVD perturbation in a face recognition task when using the FERET database.
['Wei Lu', 'Wei Sun', 'Hongtao Lu']
I am writing a research paper on a idea Enriched Gabor Feature Based PCA for Face Recognition with One Training Image per Person
[{"from": "human", "value": "I am writing a research paper on a idea Enriched Gabor Feature Based PCA for Face Recognition with 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: Gabor feature based classification approaches are widely used in face recognition, because they are insensitive to changes in illumination and facial expression. Hover, most of strategies only use the magnitude of the Gabor wavelet representation of images to generate feature vectors. When only single training image per person is available, the performance of these methods may be limited. , by making use of the slope angle as ll as the magnitude of the Gabor wavelet response,  propose a novel Enriched Gabor feature based Principal Component Analysis (EGPCA) algorithm for face recognition with one training image per person. Experiment results show that the algorithm has better performance than other methods such as (PC)$^{2}$A, E(PC)$^{2}$A and SVD perturbation in a face recognition task when using the FERET database."}]