CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of Korean Patent Application No. 10-2020-0046273 filed on Apr. 16, 2020 in the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes.

BACKGROUND

Field

The following description relates to testing a liveness using a color image and a photodiode (PD) image at the same time.

Description of Related Art

In a user authentication system, a computing device may determine whether to allow a user to access the computing device based on authentication information provided by the user. The authentication information may include a password input by the user or biometric information of the user. The biometric information includes information may relate to a fingerprint, an iris, a voice, or a face.

Recently, there has been a growing interest in face anti-spoofing technology, to improve security in the user authentication system. Face anti-spoofing determines whether a face of a user that is input into a computing device is a fake face or a genuine face. For this, features such as local binary patterns (LBP), histogram of oriented gradients (HOG), and difference of Gaussians (DoG) are extracted from an input image, and whether an input face is a fake face is determined based on the extracted features. Face spoofing is in the form of attacks using photographs, moving images, or masks, and it is important to identify such attacks in face verification.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

In one general aspect, there is provided a liveness test method, including receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

The determining may include calculating a liveness score based on a first output of the neural network with respect to the color image and a second output of the neural network with respect to the PD image, and determining the liveness of the object to be live in response to the liveness score being greater than or equal to a threshold.

The calculating may include obtaining the liveness score by adding a result of multiplying the first output by a first weight of the color image to a result of multiplying the second output by a second weight of the PD image.

The second weight may be based on a distance between the image sensor and the object.

The second weight may be based on a proportion of a region of the object to the PD image.

The second weight may be based on illuminance information of the image sensor.

The determining may include testing a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed color image, determining the liveness of the object to be fake, in response to a result of the testing of the first liveness being fake, and testing a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing of the first liveness being live.

The determining may include testing a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed color image, determining the liveness of the object to be live, in response to a result of the testing of the first liveness being live, and testing a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing of the first liveness being fake.

The determining may include testing a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed PD image, determining the liveness of the object to be fake, in response to a result of the testing of the first liveness being fake, and testing a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed color image, in response to the result of the testing of the first liveness being live.

The determining may include testing a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed PD image, determining the liveness of the object to be live, in response to a result of the testing of the first liveness being live, and testing a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed color image, in response to the result of the testing of the first liveness being fake.

In another general aspect, there is provided a liveness test apparatus, including a memory configured to store a color image and a photodiode (PD) image of an object received from an image sensor including a pixel formed of a plurality of PDs, and a processor configured to preprocess the color image and the PD image, and determine a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

The processor may be configured to calculate a liveness score based on a first output of the neural network with respect to the color image and a second output of the neural network with respect to the PD image, and determine the liveness of the object to be live in response to the liveness score being greater than or equal to a threshold.

The processor may be configured to obtain the liveness score by adding a result of multiplying the first output by a first weight of the color image to a result of multiplying the second output by a second weight of the PD image.

The second weight may be based on a proportion of a region of the object to the PD image.

The second weight may be based on illuminance information of the PD image.

The processor may be configured to test a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed color image, determine the liveness of the object to be fake, in response to a result of the testing being fake, and test a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing being live.

The processor may be configured to test a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed color image, determine the liveness of the object to be live, in response to a result of the testing of the first liveness being live, and test a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed PD image, in response to the result of the testing of the first liveness being fake.

The processor may be configured to test a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed PD image, determine the liveness of the object to be fake, in response to a result of the testing of the first liveness being fake, and test a second liveness of the object based on a second output of the neural network with respect to the result of the inputting of the preprocessed color image, in response to the result of the testing being live.

The processor may be configured to test a first liveness of the object based on a first output of the neural network with respect to the result of the inputting of the preprocessed PD image, determine the liveness of the object to be live, in response to a result of the testing of the first liveness being live, and test a second liveness of the object based on a second output of the neural network with respect to the result the inputting of the preprocessed color image, in response to the result of the testing of the first liveness being fake.

Other features and aspects will be apparent from the following detailed description, the drawings, and the claims.

