Improved canonical correlation analysis-based physiological signal fusion identity recognition method

Abstract

The invention discloses an improved canonical correlation analysis-based physiological signal fusion identity recognition method, and mainly solves the problem of relatively low identification rate of an existing method. The method is implemented by comprising the steps of 1) obtaining pulse waves and respiratory signals, and preprocessing training data; 2) intercepting waveforms of the training data, and obtaining a pulse wave training set and a respiratory training set; 3) calculating intra-class and inter-class neighborhoods of the two sets; 4) according to the intra-class and inter-class neighborhoods, calculating intra-class and inter-class correlation matrixes of the two sets and constructing a regular canonical correlation analysis objective function; 5) solving regular canonical correlation analysis-based pulse wave and respiratory conversion matrixes; 6) calculating training fusion eigenvectors by utilizing the conversion matrixes; 7) obtaining pulse wave and respiratory signal test data and calculating test fusion vectors; and 8) performing type judgment on the test fusion vectors to obtain an identity recognition result. According to the method, the identity recognition rate is increased; and the method can be applied to the e-commerce and remote medical identity authentication.

Description

technical field [0001] The invention belongs to the technical field of identification, and in particular relates to an identification method, which can be used in the fields of e-commerce, remote medical identification and the like. Background technique [0002] With the development and application of computer technology and wireless network, Internet-based e-commerce, telemedicine and other applications have developed rapidly, and have become an indispensable part of modern people's life. Because these applications involve personal property accounts, personal physiological information and other important private information, it is particularly important to ensure the safe use of these applications. Compared with single-modal biometric systems, the security of multi-modal biometric systems relies on multiple biometric features, which are difficult to be stolen or copied at the same time, so they have higher security and reliability. The canonical correlation analysis algori...

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Problems solved by technology

Finally, the effectiveness of the method is verified by carrying out experimental simulations on multiple databases. However, because the method does not take into account the influence of noise on signal feature extraction, it cannot fully obtain effective feature information, and its identification rate has room for further improvement.

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