Secure biometric metadata generation

1. A method comprising:

accessing a set of images stored on a device;

identifying one or more faces depicted in the set of images;

determining a number of facial features of each face of the one or more faces depicted in the set of images;

generating a set of face images from the set of images based on the number of facial features determined for each face of the one or more faces, each face image in the set of face images comprising a face that is aligned within one or more boundaries of the face image;

determining a set of positions of a set of facial features depicted within the set of face images;

generating a set of biometric reference maps by analyzing a combination of data associated with the set of face images and the set of positions using a machine learning model, wherein the machine learning model is trained to generate biometric reference maps based on face images and positions;

transmitting the set of face images to a reference server; and

storing the set of biometric reference maps on the device.

2. The method of claim 1 , further comprising removing the set of face images from the device in response to transmitting the set of face images to the reference server.

3. The method of claim 1 , 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.

4. The method of claim 1 , 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.

5. The method of claim 1 , 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.

6. The method of claim 1 , 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.

7. 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.

8. 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 set of images stored on a device;

identifying one or more faces depicted in the set of images;

determining a number of facial features of each face of the one or more faces depicted in the set of images;

generating a set of face images from the set of images based on the number of facial features determined for each face of the one or more faces, each face image in the set of face images comprising a face that is aligned within one or more boundaries of the face image;

determining a set of positions of a set of facial features depicted within the set of face images;

generating a set of biometric reference maps by analyzing a combination of data associated with the set of face images and the set of positions using a machine learning model, wherein the machine learning model is trained to generate biometric reference maps based on face images and positions;

transmitting the set of face images to a reference server; and

storing the set of biometric reference maps on the device.

9. The system of claim 8 , further comprising removing the set of face images from the device in response to transmitting the set of face images to the reference server.

10. The system of claim 8 , 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.

11. The system of claim 8 , 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.

12. The system of claim 8 , 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.

13. The system of claim 8 , wherein generating the set of face images from the set of images further comprising:

for each image in the set of images,

determining a number of facial features depicted in the face 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.

14. The system of claim 8 , 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 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 set of images stored on a device;

identifying one or more faces depicted in the set of images;

determining a number of facial features of each face of the one or more faces depicted in the set of images;

generating a set of face images from the set of images based on the number of facial features determined for each face of the one or more faces, each face image in the set of face images comprising a face that is aligned within one or more boundaries of the face image;

determining a set of positions of a set of facial features depicted within the set of face images;

generating a set of biometric reference maps by analyzing a combination of data associated with the set of face images and the set of positions using a machine learning model, wherein the machine learning model is trained to generate biometric reference maps based on face images and positions;

transmitting the set of face images to a reference server; and

storing the set of biometric reference maps on the device.

16. The non-transitory processor-readable storage medium of claim 15 , further comprising removing the set of face images from the device in response to transmitting the set of face images to the reference server.

17. The non-transitory processor-readable storage medium of claim 15 , 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.

18. The non-transitory processor-readable storage medium of claim 15 , 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.

19. The non-transitory processor-readable storage medium of claim 15 , 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.

20. The non-transitory processor-readable storage medium of claim 15 , 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.