Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as ll as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion.  propose a face-iris recognition method based on feature level fusion.  build a special two-dimensional- Gabor filter bank to extract local texture features from face and iris images, and then transform them by histo- gram statistics into an energy-orientation variance histogram feature with lor dimensions and higher distin- guishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a signifi- cant performance advantage. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.6.063020)
['Guang Huo', 'Yuanning Liu', 'Xiaodong Zhu', 'Hongxing Dong', 'Fei He']
I am writing a research paper on a idea Face–iris multimodal biometric scheme based on feature level fusion
[{"from": "human", "value": "I am writing a research paper on a idea Face\u2013iris multimodal biometric scheme based on feature level fusion"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as ll as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion.  propose a face-iris recognition method based on feature level fusion.  build a special two-dimensional- Gabor filter bank to extract local texture features from face and iris images, and then transform them by histo- gram statistics into an energy-orientation variance histogram feature with lor dimensions and higher distin- guishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a signifi- cant performance advantage. \u00a9 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.6.063020)"}]