Biological recognition method and device
A biometric, to-be-identified technology, applied in character and pattern recognition, protocol authorization, data processing applications, etc., can solve problems such as difficult single-modal biometric identification technology identification technology performance requirements, and improve the success of identification. rate effect
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Embodiment 1
[0030] Based on the principles of the above invention, embodiments of the present invention provide a biometric identification method, such as figure 1 As shown, the method includes:
[0031] S101. Obtain n types of biometric features of a user to be identified. Among them, n≥2.
[0032] Specifically, in the present invention, the n types of biometric features may include: fingerprint features, iris features, facial features, palmprint features, voiceprint features, and any number of features that can be used to distinguish user identities. The present invention may not be limited to this.
[0033] S102. Acquire characteristic parameters of n types of biological characteristics of the user to be identified.
[0034] Specifically, in order to facilitate the subsequent model training, in the present invention, a specific algorithm can be used to convert the acquired n types of biological characteristics into a one-dimensional sequence of preset lengths, which can be used as c...
Embodiment 2
[0078] An embodiment of the present invention provides a biometric identification device, and the biometric identification device is used to execute the above biometric identification method. Such as Figure 4 As shown in , it is a schematic diagram of a possible structure of the biometric identification device provided by the embodiment of the present invention. Specifically, the biometric identification device 20 includes: an acquisition unit 201 , a fusion feature calculation unit 202 , an integrated feature calculation unit 203 , and an identification unit 204 . in:
[0079] An acquisition unit 201, configured to acquire characteristic parameters of n biological characteristics of the user to be identified, where n≥2;
[0080] The fusion feature calculation unit 202 is used to use the long short-term memory neural network LSTM model to fuse the feature parameters of the n biological features respectively to generate high-order feature parameters corresponding to the n bi...
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