Biological feature recognition performance index prediction method based on statistical learning
A biometric recognition and biometric technology, applied in character and pattern recognition, computing, computer parts and other directions, can solve the problems of difficulty in model parameter calculation, lack of universality, and inability to adapt to biometric recognition performance evaluation, etc. Predict accurate, computationally simple effects
Active Publication Date: 2011-09-21
INST OF AUTOMATION CHINESE ACAD OF SCI
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The research on predictive models based on sample quality has just started, such as the performance prediction of face recognition algorithms, but the model involves complex statistical distribution functions, and the calculation of model parameters is quite difficult, and it is not universal, and cannot be adapted to the performance of biometric recognition. Assessment Task Needs
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[0021] The detailed issues involved in the technical solution of the present invention will be described in detail below by taking the performance prediction flow of a certain fingerprint identification system as an example in conjunction with the accompanying drawings. It should be pointed out that the described embodiments are only intended to facilitate the understanding of the present invention, rather than limiting it in any way.
[0022] Description of the problem: A fingerprint identification company C has developed a fingerprint identification system FS, and built an internal fingerprint database FD during the research and development process, which stores the fingerprint images of all employees of the company. The test results of FS on FD are good, but C hopes to use FS for attendance management of construction workers. Obviously, the characteristics of fingerprint images of construction workers are far from those of employees of high-tech companies, so C hopes to pre...
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The invention relates to a biological feature recognition performance index prediction method based on statistical learning, and the method comprises the following steps: performing quality evaluation on biological feature training samples; calculating true matching fractions of a biological feature recognition system on a training database; fitting gaussian distribution of the true matching fractions among biological feature samples with various quality combinations; performing sampling statistics on the composition proportion of the biological feature samples of various quality grades in anapplication environment; constructing a hybrid gaussian model according to steps c and d so as to estimate the distribution of the true matching fractions of the biological feature recognition systemin the application environment; and predicting the performance index and confidence interval of the biological feature recognition system. By adopting the method, the calculation is simple, the prediction is precision, the method is applicable to a plurality of biological feature modes, and the universal biological feature recognition performance prediction is realized.
Description
technical field [0001] The invention relates to biometric feature recognition, image processing, pattern recognition and statistics, especially the field of biometric feature recognition performance evaluation. Background technique [0002] Safety is a major issue of global concern, and reliable personal identification is an important technical means to ensure personal safety and public safety. It is against this background that a variety of biometric technologies such as iris, face, and fingerprint recognition have developed. Biometric technology has been increasingly used in various fields related to national and social security, such as public security, border inspection, finance, social security, access control, etc. [0003] Due to certain changes in the biometric data collected by different time, different environments, different postures, and different devices, and the comparison of biometrics is based on probability and statistical science, the recognition algorithm...
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Inventor 谭铁牛孙哲南何倩
Owner INST OF AUTOMATION CHINESE ACAD OF SCI