Partial face detection for video conferencing

1. A device, comprising:

at least one processor; and

storage accessible to the at least one processor and comprising instructions executable by the at least one processor to:

during deployment of a model, provide at least a first image to the model as input to the model, the model trained using groups of training images that show respective different parts of faces of one or more people but not full faces of the one or more people;

receive, as output from the model, an indication of a facial body part shown in the first image; and

based on the indication, present a notification to a participant of a video conference, the notification indicating at least one action for the participant to take for the participant's full face to be shown in a video stream as part of the video conference;

wherein the instructions are executable to:

train the model using the groups of training images that show respective different parts of faces of the one or more people but not full faces of the one or more people, wherein the model is trained using at least one group of training images that show: ceilings but no facial body parts, keyboards but no facial body parts, desk tops but no facial body parts, and/or table tops but no facial body parts;

wherein the notification is also presented based on input from a sensor other than a camera;

wherein the input from the sensor indicates a laptop lid angle, the notification indicating an adjustment to make to the laptop lid angle.

2. The device of claim 1 , wherein the instructions are executable to:

train the model using at least one group of training images that show ceilings but no facial body parts.

3. The device of claim 1 , wherein the instructions are executable to:

train the model using at least one group of training images that show keyboards but no facial body parts.

4. The device of claim 1 , wherein the instructions are executable to:

train the model using at least one group of training images that show one or more of: desk tops but no facial body parts, table tops but no facial body parts.

5. A method, comprising:

providing at least a first image to a model as first input, the model trained using groups of training images that show respective different parts of faces of one or more people but not full faces of the one or more people;

receiving, as output from the model, an indication regarding a facial body part; and

based on the indication and based on second input from a sensor other than a camera, presenting a notification to a participant of a video conference, the notification indicating at least one action for the participant to take for the participant's full face to be shown in a video stream as part of the video conference;

wherein the second input from the sensor indicates a laptop lid angle, the notification indicating an adjustment to make to the laptop lid angle.

6. At least one computer readable storage medium (CRSM) that is not a transitory signal, the at least one computer readable storage medium comprising instructions executable by at least one processor to:

provide at least a first image to a model as first input, the model trained using groups of training images that show respective different parts of faces of one or more people but not full faces of the one or more people;

receive, as output from the model, an indication regarding a facial body part; and

based on the indication and based on second input from a sensor other than a camera, present a notification during a video conference, the notification indicating at least one action to take for a full face to be shown in a video stream as part of the video conference;

wherein the second input from the sensor indicates a laptop lid angle, the notification indicating an adjustment to make to the laptop lid angle.

7. The at least one CRSM of claim 6 , wherein the second input from the sensor indicates a device location at a public place, the notification being presented based at least in part on the device location at the public place.

8. The at least one CRSM of claim 7 , wherein the instructions are executable to:

based on the device location at the public place, execute eye tracking to identify an engaged user; and

based on the identification of the engaged user, present the notification indicating at least one action for the engaged user to take.

9. The at least one CRSM of claim 8 , wherein the identification of the engaged user is executed based on identification of user engagement for at least a threshold amount of time.

10. The at least one CRSM of claim 6 , wherein the sensor is a global positioning system (GPS) sensor.

11. The at least one CRSM of claim 6 , wherein the sensor is a microphone, and wherein the instructions are executable to:

execute natural language understanding (NLU) using input from the microphone to determine that a first user is discussing a topic associated with the video conference; and

based on the determination, present the notification, the notification indicating at least one action for the first user to take.

12. The device of claim 1 , wherein the input from the sensor indicates a device location at a public place, the notification being presented based at least in part on the device location at the public place.

13. The device of claim 1 , wherein the input from the sensor indicates the participant as speaking about a topic associated with the video conference, and wherein the notification is presented based on identification of the participant as speaking about the topic.

14. The method of claim 5 , wherein the sensor comprises a potentiometer.

15. The method of claim 5 , wherein the sensor comprises a proximity sensor.

16. The method of claim 5 , wherein the sensor comprises an accelerometer.