Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  analyze the performance of an iris based recognition system.  consider a practical setting where matching scores are accessible for collecting data.  assume that multiple scans from the same iris are available and design the decision rules based on this assumption.  show that vectors of matching scores are described by a Gaussian model with dependent components both under the genuine and imposter hypotheses. Two test statistics: the average Hamming distance and the log-likelihood ratio are designed.  show that the log-likelihood ratio with ll estimated maximum likelihood parameters in it outperforms the first test statistic.  further use an empirical approach, Chernoff bound, and large deviations approach to predict the performance of the recognition system.
['Natalia A. Schmid', 'Bojan Cukic', 'Manasi V. Ketkar']
I am writing a research paper on a idea Performance analysis of iris based identification system at the matching score level
[{"from": "human", "value": "I am writing a research paper on a idea Performance analysis of iris based identification system at the matching score level"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  analyze the performance of an iris based recognition system.  consider a practical setting where matching scores are accessible for collecting data.  assume that multiple scans from the same iris are available and design the decision rules based on this assumption.  show that vectors of matching scores are described by a Gaussian model with dependent components both under the genuine and imposter hypotheses. Two test statistics: the average Hamming distance and the log-likelihood ratio are designed.  show that the log-likelihood ratio with ll estimated maximum likelihood parameters in it outperforms the first test statistic.  further use an empirical approach, Chernoff bound, and large deviations approach to predict the performance of the recognition system."}]