Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The design of a recognition system requires careful attention to pattern representation and classifier design. Some statistical approaches choose those features, in a d-dimensional initial space, which allow sample vectors belonging to different categories to occupy compact and disjoint regions in a low-dimensional subspace. The effectiveness of the representation subspace is then determined by how ll samples from different classes can be separated. ,  propose a feature selection process that sorts the principal components, generated by principal component analysis, in the order of their importance to solve a specific recognition task. This method provides a low-dimensional representation subspace which has been optimized to improve the classification accuracy.  focus on the problem of facial expression recognition to demonstrate this technique.  also propose a decision tree-based classifier that provides a ‘‘coarse-to-fine’’ classification of new samples by successive projections onto more and more precise representation subspaces. Results confirm, first, that the choice of the representation strongly influences the classification results, second that a classifier has to be designed for a specific representation. r 2002 Elsevier Science B.V. All rights reserved.
['Séverine Dubuisson', 'Franck Davoine', 'Mylène Masson']
I am writing a research paper on a idea A solution for facial expression representation and recognition
[{"from": "human", "value": "I am writing a research paper on a idea A solution for facial expression representation and recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The design of a recognition system requires careful attention to pattern representation and classifier design. Some statistical approaches choose those features, in a d-dimensional initial space, which allow sample vectors belonging to different categories to occupy compact and disjoint regions in a low-dimensional subspace. The effectiveness of the representation subspace is then determined by how ll samples from different classes can be separated. ,  propose a feature selection process that sorts the principal components, generated by principal component analysis, in the order of their importance to solve a specific recognition task. This method provides a low-dimensional representation subspace which has been optimized to improve the classification accuracy.  focus on the problem of facial expression recognition to demonstrate this technique.  also propose a decision tree-based classifier that provides a \u2018\u2018coarse-to-fine\u2019\u2019 classification of new samples by successive projections onto more and more precise representation subspaces. Results confirm, first, that the choice of the representation strongly influences the classification results, second that a classifier has to be designed for a specific representation. r 2002 Elsevier Science B.V. All rights reserved."}]