Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel Bilinear Feature Line Analysis (BFLA) is proposed for image feature extraction in this letter. Neaerest feature line (NFL) is a porful classifier. Some NFL based subspace algorithms have been introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For face recognition, face samples should be transformed to vectors firstly. This process induces a high computational complexity and also may lead to the loss of the geometric feature of samples. The proposed BFLA is a matrix-based algorithm. It aims to minimize the within class scatter based on two-dimensional NFL. The experimental results on Yale face databases confirm its effectiveness.
['Lijun Yan', 'Jianhui Zhang', 'Jeng-Shyang Pan', 'Lin-Lin Tang']
I am writing a research paper on a idea Bilinear Feature Line Analysis for Face Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Bilinear Feature Line 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 novel Bilinear Feature Line Analysis (BFLA) is proposed for image feature extraction in this letter. Neaerest feature line (NFL) is a porful classifier. Some NFL based subspace algorithms have been introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For face recognition, face samples should be transformed to vectors firstly. This process induces a high computational complexity and also may lead to the loss of the geometric feature of samples. The proposed BFLA is a matrix-based algorithm. It aims to minimize the within class scatter based on two-dimensional NFL. The experimental results on Yale face databases confirm its effectiveness."}]