Artificial intelligence for passive liveness detection
Methods, systems, and storage media are disclosed for passive liveness detection using artificial intelligence. Example implementations may receive an image of a person's face; generate a cropped version of that image; generate two different embeddings using two convolutional neural networks that are fed the image and the cropped image, respectively; generate a combined embedding that is a concatenation of the two embeddings; and generate, based on the combined embedding, an output indicating whether the facial portion corresponds to a live person. In addition, systems, devices, and methods for multi-factor authentication for transaction processing are provided. A point-of-transaction device captures customer information, biometric data, and images of identification documents and transmits the information to a transaction information server which receives the transaction request, queries one or more storage records to confirm the identity of the customer to the transaction and to determine whether the customer is authorized to engage in the transaction. The point-of-transaction device communicates a transaction identifier code and at least a portion of the transaction request to a transaction authority. The transaction authority transmits a confirmation signal to the point-of-transaction device based on the transaction identifier code and the transaction request.
1 . A method that more accurately detects passive liveness using a plurality of convolutional neural networks and concatenating, the method comprising:
receiving, by a computing device, an input image, wherein the input image comprises a facial portion and a first background portion;
generating, based on the input image, a cropped image, wherein the cropped image comprises the facial portion and a second background portion that is a subset of the first background portion;
generating, based on the input image and via a first convolutional neural network, a first image embedding, wherein the first convolutional neural network comprises an average pooling layer, a fully connected layer, and a plurality of depthwise convolutional layers, and wherein the first convolutional neural network comprises a first plurality of layers and a first plurality of input channels, wherein each input channel of the first plurality of input channels corresponds to a layer of the first plurality of layers;
generating, based on the cropped image and via a second convolutional neural network, a second image embedding, wherein the second convolutional neural network comprises a second plurality of input channels, wherein each input channel of the second plurality of input channels is determined by reducing a corresponding input channel of the first plurality of input channels;
concatenating the first image embedding and the second image embedding;
generating, via the concatenation of the first image embedding and the second image embedding, a combined embedding;
after the concatenating, generating, based on the combined embedding, output indicating whether the facial portion corresponds to a live person, wherein the output more accurately detects liveness than when the concatenating step is omitted; and
denying, based on the output indicating whether the facial portion corresponds to the live person, access to a computer system.
2 . The method of claim 1 , wherein a first width parameter corresponding to input channels of the first convolutional neural network is greater than a second width parameter corresponding to input channels of the second convolutional neural network.
3 . The method of claim 1 , wherein the generating the cropped image comprises:
removing, from the input image, pixels corresponding to the second background portion.
4 . The method of claim 1 , further comprising:
training, based on a first plurality of images and a second plurality of cropped images, the first convolutional neural network and the second convolutional neural network, to output information that indicates liveness of each person in the first plurality of images.
5 . The method of claim 1 , further comprising:
receiving an additional image for liveness detection, wherein the additional image comprises a person; and
determining, based on facial features of the person, that the additional image is not suitable for liveness detection.
6 . The method of claim 1 , wherein the generating, based on the first image embedding and the second image embedding, output comprises generating the output via a sigmoid function.
7 . A method comprising:
generating, by a computing device and via a camera, a plurality of images, wherein each image of the plurality of images indicates a same person with a background, and wherein each image of the plurality of images is generated within a threshold time of each other;
generating, via a first convolutional neural network and based on the plurality of images, a first image embedding, wherein the first convolutional neural network comprises an average pooling layer and a fully connected layer, and wherein the first convolutional neural network comprises a first plurality of layers and a first plurality of input channels, wherein each input channel of the first plurality of input channels corresponds to a layer of the first plurality of layers;
cropping each image of the plurality of images by removing a portion of the background;
generating, via a second convolutional neural network and based on the plurality of cropped images, a second image embedding, wherein the second convolutional neural network comprises a second plurality of input channels, wherein each input channel of the second plurality of input channels is determined by reducing a corresponding input channel of the first plurality of input channels;
concatenating the first image embedding and the second image embedding to generate a combined embedding;
generating, based on the combined embedding, an output value that more accurately detects liveness of the same person in the plurality of images than when the concatenating step is omitted; and
granting, to a user device and based on the output value, access to a computing system.
8 . The method of claim 7 , wherein the first convolutional neural network comprises a recurrent convolutional neural network.
9 . The method of claim 7 , wherein the by removing the portion of the background comprises removing one or more pixels corresponding to a background portion of a first image of the plurality of images.
10 . The method of claim 7 , further comprising:
training, based on the plurality of images and the plurality of cropped images, the first and second convolutional neural networks, to output information that indicates the liveness of the same person.
11 . The method of claim 7 , further comprising:
receiving an additional image for liveness detection, wherein the additional image comprises a person; and
determining, based on facial features of the person, that the additional image is not suitable for liveness detection.
12 . The method of claim 7 , wherein the generating the output value comprises generating the output value via a sigmoid function.
13 . A method comprising:
receiving, by a computing device, an input image, wherein the input image comprises a facial portion and a first background portion;
generating, based on the input image, a cropped image, wherein the cropped image comprises a subset of pixels of the input image;
generating, based on the input image and via a first convolutional neural network, a first image embedding, wherein the first convolutional neural network comprises a first plurality of layers and a first plurality of input channels, wherein each input channel of the first plurality of input channels corresponds to a layer of the first plurality of layers;
generating, based on the cropped image and via a second convolutional neural network, a second image embedding, wherein the second convolutional neural network comprises a second plurality of input channels, wherein each input channel of the second plurality of input channels is determined by reducing a corresponding input channel of the first plurality of input channels;
concatenating the first image embedding and the second image embedding;
generating, via the concatenation, a combined embedding;
generating, based on the combined embedding, output indicating whether the facial portion corresponds to a live person, wherein the output more accurately detects liveness than when the concatenating step is omitted; and
denying, based on the output indicating whether the facial portion corresponds to the live person, access to a computer system.
14 . The method of claim 13 , further comprising:
training, based on the input image and the cropped image, the first convolutional neural network and the second convolutional neural network, to output information that indicates liveness of the live person in the input image.
15 . The method of claim 13 , further comprising:
receiving an additional image for liveness detection, wherein the additional image indicates a person; and
determining, based on facial features of the person, that the additional image is not suitable for liveness detection.
16 . The method of claim 13 , wherein the generating the output comprises generating the output via a sigmoid function, and the method further comprising: granting, based on the output, access to the computer system.