Secure biometric metadata generation
Systems, devices, media and methods are presented for generating biometric image data. In one example, a system accesses a set of images stored on a mobile computing device. The system identifies one or more faces depicted in the set of images and generates a set of face images from the set of images. The system determines a set of positions of a set of facial features depicted within the set of face images and generates a set of biometric reference maps based on the set of positions. The system transmits the set of face images to a reference server and stores the set of biometric reference maps on the mobile computing device.
1. A method comprising:
accessing a first image stored on a device;
determining a set of positions for a set of facial features depicted within the first image;
generating a set of biometric reference maps by inputting the set of positions for the set of facial features into a machine learning model to receive the set of biometric reference maps from the machine learning model based on the inputted set of positions, wherein the machine learning model is trained to generate biometric reference maps based on face images and positions; and
applying the set of biometric reference maps on a second image.
2. The method of claim 1 , further comprising:
accessing a set of images, wherein the set of images comprises the first image; and
determining a set of positions for a set of facial features depicted within the set of images, wherein generating the set of biometric reference maps is based on the inputting of the set of positions.
3. The method of claim 1 , further comprising:
identifying one or more faces depicted in a set of images; and
generating a set of face images from the set of images based on facial features determined for each face of the one or more faces.
4. The method of claim 3 , wherein each face image in the set of face images comprising a face that is aligned within one or more boundaries of the face image.
5. The method of claim 3 , further comprising:
determining the facial features of each face of the one or more faces depicted in the set of images.
6. The method of claim 3 , further comprising:
transmitting the set of face images to a reference server.
7. The method of claim 6 , further comprising removing the set of face images from the device in response to transmitting the set of face images to the reference server.
8. The method of claim 3 , wherein generating the set of face images from the set of images further comprising:
generating a set of thumbnail images, wherein each thumbnail image includes a single face.
9. The method of claim 3 , wherein generating the set of face images from the set of images further comprising:
for each image in the set of images,
determining whether a number of facial features depicted in the image exceeds a predetermined threshold value; and
in response to determining that the number of facial features depicted in the image does not exceed the predetermined threshold value, disregarding a face depicted in the image.
10. The method of claim 3 , wherein identifying the one or more faces in the set of images further comprises:
identifying a set of coordinates associated with the one or more faces in the set of images, and
generating one or more bounding boxes around each face of the one or more faces.
11. The method of claim 1 , further comprising:
transmitting the set of biometric reference maps to a reference server.
12. The method of claim 1 , further comprising:
storing the set of biometric reference maps on the device.
13. The method of claim 12 , wherein storing the set of biometric reference maps further comprises:
encrypting the set of biometric reference maps to generate an encrypted set of biometric reference maps; and
storing the encrypted set of biometric reference maps on the device.
14. The method of claim 1 , wherein generating the set of biometric reference maps further comprises:
analyzing the set of face images and the set of positions using a machine learning model trained to identify the faces depicted in the face images.
15. A system comprising:
one or more processors; and
a non-transitory processor-readable storage medium storing processor executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
accessing a first image stored on a device;
determining a set of positions for a set of facial features depicted within the first image;
generating a set of biometric reference maps by inputting the set of positions for the set of facial features into a machine learning model to receive the set of biometric reference maps from the machine learning model based on the inputted set of positions, wherein the machine learning model is trained to generate biometric reference maps based on face images and positions; and
applying the set of biometric reference maps on a second image.
16. The system of claim 15 , the operations further comprising:
accessing a set of images, wherein the set of images comprises the first image; and
determining a set of positions for a set of facial features depicted within the set of images, wherein generating the set of biometric reference maps is based on the inputting of the set of positions.
17. The system of claim 15 , the operations further comprising:
identifying one or more faces depicted in a set of images; and
generating a set of face images from the set of images based on facial features determined for each face of the one or more faces.
18. The system of claim 17 , wherein each face image in the set of face images comprising a face that is aligned within one or more boundaries of the face image.
19. The system of claim 17 , wherein comprising:
determining the facial features of each face of the one or more faces depicted in the set of images.
20. A non-transitory processor-readable storage medium storing processor executable instructions that, when executed by a processor of a machine, cause the machine to perform operations comprising:
accessing a first image stored on a device;
determining a set of positions for a set of facial features depicted within the first image;
generating a set of biometric reference maps by inputting the set of positions for the set of facial features into a machine learning model to receive the set of biometric reference maps from the machine learning model based on the inputted set of positions, wherein the machine learning model is trained to generate biometric reference maps based on face images and positions; and
applying the set of biometric reference maps on a second image.