Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a new statistical projection-based method called Two-Dimensional- Oriented Linear Discriminant Analysis (2DO-LDA) is presented. While in the Fisherfaces method the 2D image matrices are first transformed into 1D vectors by merging their rows of pixels, 2DO-LDA is directly applied on matrices, as 2D-PCA. Within and beten-class image covariance matrices are generalized, and 2DO-LDA aims at finding a projection space jointly maximizing the second and minimizing the first by considering a generalized Fisher criterion defined on image matrices. A series of experiments was performed on various face image databases in order to evaluate and compare the effectiveness and robustness of 2DO-LDA to 2D-PCA and the Fisherfaces method. The experimental results indicate that 2DO-LDA is more efficient than both 2D-PCA and LDA when dealing with variations in lighting conditions, facial expression and head pose.
['Muriel Visani', 'Christophe Garcia', 'Jean-Michel Jolion']
I am writing a research paper on a idea Two-Dimensional-Oriented Linear Discriminant Analysis for Face Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Two-Dimensional-Oriented Linear Discriminant Analysis for Face Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a new statistical projection-based method called Two-Dimensional- Oriented Linear Discriminant Analysis (2DO-LDA) is presented. While in the Fisherfaces method the 2D image matrices are first transformed into 1D vectors by merging their rows of pixels, 2DO-LDA is directly applied on matrices, as 2D-PCA. Within and beten-class image covariance matrices are generalized, and 2DO-LDA aims at finding a projection space jointly maximizing the second and minimizing the first by considering a generalized Fisher criterion defined on image matrices. A series of experiments was performed on various face image databases in order to evaluate and compare the effectiveness and robustness of 2DO-LDA to 2D-PCA and the Fisherfaces method. The experimental results indicate that 2DO-LDA is more efficient than both 2D-PCA and LDA when dealing with variations in lighting conditions, facial expression and head pose."}]