Facial recognition for drivers
Methods for performing improving facial recognition of a driver in a vehicle are disclosed. A set of images is received. Each of the set of images includes a portion that is identified as a face. The identification is based on an application of a first machine-learned model to each of the set of images. The application of the first machine-learned model is performed by an application associated with a client camera device mounted in a vehicle. Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured.
1. A system comprising:
one or more memories;
one or more computer processors;
a set of instructions incorporated into the one or more memories, the set of instructions configuring the one or more computer processors to perform operations, the operations comprising:
receiving, at a server application executing on an application server, a set of images from an application executing on a client device;
identifying an issue with a quality of the set of images;
identifying a change to a parameter of the application that will improve a quality of an additional set of images received from the application; and
communicating the change to cause a reconfiguring of the application.
2. The system of claim 1 , wherein the issue with the quality of the set of images pertains to a failure to identify a recognizable face at least a certain percentage of times during a capturing of the set of images.
3. The system of claim 1 , wherein the issue with the quality of the set of images pertains to a failure to obtain a sufficient number of angles of a recognizable face for training of a facial recognition algorithm.
4. The system of claim 1 , wherein the change to the parameter of the application pertains to a number of images that will be captured in the additional set of images when a triggering event occurs.
5. The system of claim 1 , wherein the change to the parameter of the application pertains to a time period between images that will be captured in the additional set of images when a triggering event occurs.
6. The system of claim 1 , wherein the change to the parameter of the application pertains to a file format that will be used for the additional set of images.
7. The system of claim 1 , wherein the identifying of the change includes applying a machine-learned model designed to optimize configuration parameters of the application.
8. A method comprising:
receiving, at a server application executing on an application server, a set of images from an application executing on a client device;
identifying an issue with a quality of the set of images;
identifying a change to a parameter of the application that will improve a quality of an additional set of images received from the application; and
communicating the change to cause a reconfiguring of the application.
9. The method of claim 8 , wherein the issue with the quality of the set of images pertains to a failure to identify a recognizable face at least a certain percentage of times during a capturing of the set of images.
10. The method of claim 8 , wherein the issue with the quality of the set of images pertains to a failure to obtain a sufficient number of angles of a recognizable face for training of a facial recognition algorithm.
11. The method of claim 8 , wherein the change to the parameter of the application pertains to a number of images that will be captured in the additional set of images when a triggering event occurs.
12. The method of claim 8 , wherein the change to the parameter of the application pertains to a time period between images that will be captured in the additional set of images when a triggering event occurs.
13. The method of claim 8 , wherein the change to the parameter of the application pertains to a file format that will be used for the additional set of images.
14. The method of claim 8 , wherein the identifying of the change includes applying a machine-learned model designed to optimize configuration parameters of the application.
15. A non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more computer processors, causes the one or more computer processors to perform operations, the operations comprising:
receiving, at a server application executing on an application server, a set of images from an application executing on a client device;
identifying an issue with a quality of the set of images;
identifying a change to a parameter of the application that will improve a quality of an additional set of images received from the application; and
communicating the change to cause a reconfiguring of the application.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the issue with the quality of the set of images pertains to a failure to identify a recognizable face at least a certain percentage of times during a capturing of the set of images.
17. The non-transitory computer-readable storage medium of claim 15 , wherein the issue with the quality of the set of images pertains to a failure to obtain a sufficient number of angles of a recognizable face for training of a facial recognition algorithm.
18. The non-transitory computer-readable storage medium of claim 15 , wherein the change to the parameter of the application pertains to a number of images that will be captured in the additional set of images when a triggering event occurs.
19. The non-transitory computer-readable storage medium of claim 15 , wherein the change to the parameter of the application pertains to a time period between images that will be captured in the additional set of images.
20. The non-transitory computer-readable storage medium of claim 15 , wherein the change to the parameter of the application pertains to a file format that will be used for the additional set of images when a triggering event occurs.