Method for training fingerprint anti-counterfeiting neural network, method for fingerprint anti-counterfeiting, apparatus for training fingerprint anti-counterfeiting neural network, and apparatus for fingerprint anti-counterfeiting

A method for fingerprint anti-counterfeiting, comprising:

• obtaining raw domain data of a to-be-detected fingerprint;
• preprocessing the raw domain data of the to-be-detected fingerprint via a preprocessing subnetwork of a target classification network to obtain a seventh feature description matrix;
• performing feature extraction on the seventh feature description matrix via a feature extraction subnetwork of the target classification network to obtain an eighth feature description matrix;
• determining a ninth feature description matrix matching the eighth feature description matrix in a fingerprint template feature library, wherein a plurality of feature description matrices of a real finger entered in a fingerprint template are stored in the fingerprint template feature library;
• fusing the eighth feature description matrix and the ninth feature description matrix to obtain a fused feature matrix;
• and
• inputting the fused feature matrix into a classification subnetwork of the target classification network to obtain an authenticity recognition result of the to-be-detected fingerprint, wherein the authenticity recognition result is used to indicate whether the to-be-detected fingerprint is a fingerprint of the real finger or a fingerprint of a prosthetic finger.

An apparatus for training a fingerprint anti-counterfeiting neural network, comprising:

• a first obtaining module configured to obtain a plurality of groups of training data, each group of the training data comprising:
• first raw domain data, second raw domain data, and third raw domain data, wherein the first raw domain data is, when a real finger is entered in a fingerprint template, collected raw data of the real finger, the second raw domain data is raw data of the real finger collected during fingerprint verification using the real finger, and the third raw domain data is raw data collected during fingerprint verification using a prosthetic finger in place of the real finger;
• and
• a training module configured to train an initial classification network using the plurality of groups of training data to obtain a target classification network, wherein the initial classification network comprises a fusion subnetwork and a classification subnetwork, and for each group of the training data, the fusion subnetwork is configured to generate a first fingerprint matching pair based on a feature description matrix of the first raw domain data and the second raw domain data, and generate a second fingerprint matching pair based on a feature description matrix of the first raw domain data and the third raw domain data, and the classification subnetwork is configured to perform fingerprint classification and recognition based on the first fingerprint matching pair and the second fingerprint matching pair.