Method and apparatus for fake fingerprints generation, and training method of artificial intelligence for identifying fake fingerprints

1 . A method for training a fake fingerprint detection model, performed by a training device for the fake fingerprint detection model, the method comprising:

acquiring a physical fake fingerprint image generated in a physical manner targeting a finger;

providing generation constraints and a unique identification information to a training fingerprint image generation model;

generating a training fingerprint image to be used for training the fake fingerprint detection model by using the training fingerprint image generation model; and

training the fake fingerprint detection model using the physical fake fingerprint image and the training fingerprint image in a transfer learning manner or an ensemble learning manner,

wherein the training fingerprint image generation model is trained to generate different training fingerprint images based on conditions included in the generation constraints.

2 . The method of claim 1 , wherein in the transfer learning manner, the physical fake fingerprint image is used for pre-training and the training fingerprint image is used for fine-tuning.

3 . The method of claim 2 , wherein in the pre-training, an image with masking applied to a portion of the physical fake fingerprint image is provided as an input for training, and an image with no masking applied is provided as a correct answer for training.

4 . The method of claim 1 , wherein the fake fingerprint detection model includes a physical fake fingerprint detection model and a training fake fingerprint detection model, and wherein in the training of the ensemble learning manner, the physical fake fingerprint detection model and the training fake fingerprint detection model are trained separately, the physical fake fingerprint detection model is trained by receiving the physical fake fingerprint image as an input for training and receiving whether the received image is fake or not as a correct answer for training, and the training fake fingerprint detection model is trained by receiving the training fingerprint image as an input for training and receiving whether the received image is fake or not as a correct answer for training.

5 . The method of claim 1 , wherein the unique identification information includes at least one of first identification information based on start points, end points and branch points of ridges in a fingerprint of the finger, second identification information based on orientations of the ridges, and third identification information based on a distribution density of the ridges.

6 . The method of claim 5 , wherein the generation constraints include information about a sensor that identifies the fingerprint, information about surrounding environment in which the fingerprint is identified, and labeling information about the fingerprint.

7 . The method of claim 6 , wherein the labeling information includes information about a type of physical means used to generate the physical fake fingerprint image.

8 . The method of claim 1 , wherein the training fingerprint image generation model acquires random noise and generates different training fingerprint images according to the random noise even when same generation constraints are applied.

9 . A device for training a fake fingerprint detection model, the device comprising:

a memory storing computer-executable instructions; and

a processor for executing the instructions to:

acquire a physical fake fingerprint image generated in a physical manner targeting a finger;

provide generation constraints and a unique identification information to a training fingerprint image generation model;

generate a training fingerprint image to be used for training the fake fingerprint detection model by using the training fingerprint image generation model; and

train the fake fingerprint detection model using the physical fake fingerprint image and the training fingerprint image in a transfer learning manner or an ensemble learning manner,

wherein the training fingerprint image generation model is trained to generate different training fingerprint images based on conditions included in the generation constraints.

10 . The device of claim 9 , wherein in the transfer learning manner, the physical fake fingerprint image is used for pre-training and the training fingerprint image is used for fine-tuning.

11 . The device of claim 10 , wherein in the pre-training, an image with masking applied to a portion of the physical fake fingerprint image is provided as an input for training, and an image with no masking applied is provided as a correct answer for training.

12 . The device of claim 9 , wherein the fake fingerprint detection model includes a physical fake fingerprint detection model and a training fake fingerprint detection model, and wherein in the training of the ensemble learning manner, the physical fake fingerprint detection model and the training fake fingerprint detection model are trained separately, the physical fake fingerprint detection model is trained by receiving the physical fake fingerprint image as an input for training and receiving whether the received image is fake or not as a correct answer for training, and the training fake fingerprint detection model is trained by receiving the training fingerprint image as an input for training and receiving whether the received image is fake or not as a correct answer for training.

13 . The device of claim 9 , wherein the unique identification information includes at least one of first identification information based on start points, end points and branch points of ridges in a fingerprint of the finger, second identification information based on orientations of the ridges, and third identification information based on a distribution density of the ridges.

14 . The device of claim 13 , wherein the generation constraints include information about a sensor that identifies the fingerprint, information about surrounding environment in which the fingerprint is identified, and labeling information about the fingerprint.

15 . The device of claim 14 , wherein the labeling information includes information about a type of physical means used to generate the physical fake fingerprint image.

16 . The device of claim 9 , wherein the training fingerprint image generation model acquires random noise and generates different training fingerprint images according to the random noise even when same generation constraints are applied.

17 . A non-transitory computer-readable recording medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the processor to perform a method for training a fake fingerprint detection model, the method comprising:

acquiring a physical fake fingerprint image generated in a physical manner targeting a finger;

providing generation constraints and a unique identification information to a training fingerprint image generation model;

generating a training fingerprint image to be used for training the fake fingerprint detection model by using the training fingerprint image generation model; and

training the fake fingerprint detection model using the physical fake fingerprint image and the training fingerprint image in a transfer learning manner or an ensemble learning manner,

wherein the training fingerprint image generation model is trained to generate different training fingerprint images based on conditions included in the generation constraints.