Region-of-interest imaging and identifying eye features

Patent metadata

Field	Value
Publication number	US-2025028389-A1
Application number	US-202418909167-A
Country	US
Kind code	A1
Filing date	Oct 8, 2024
Priority date	Aug 26, 2022
Publication date	Jan 23, 2025
Grant date	—

Abstract

Imaging signals are generated in response to image light. Event signals are generated in response to receive the imaging signals from imaging pixels. A Region of Interest (ROI) of the imaging pixels is identified from a spatial concentration of event signals in the ROI of imaging pixels within a time period. An ROI portion of the imaging pixels in the ROI are driven to capture an ROI image frame.

First claim

What is claimed is:

1 . An imaging sensor comprising:
imaging pixels configured to generate imaging signals in response to image light; event-sensing logic configured to generate event signals in response to receiving the imaging signals from the imaging pixels; and region-of-interest (ROI) logic coupled to receive the event signals from the event-sensing logic, wherein the ROI logic is configured to:
identify an ROI of the imaging pixels from a spatial concentration of event signals in the ROI within a time period; and
drive an ROI portion of the imaging pixels in the ROI to capture an ROI image frame.

2 . The image sensor of claim 1 , wherein the event signals are generated asynchronously, and wherein the ROI image frame is captured with a global shutter or rolling shutter of the ROI portion of the imaging pixels.

3 . The image sensor of claim 1 , wherein the imaging pixels are configured to sense near-infrared light and reject visible light.

4 . The image sensor of claim 1 , wherein the event-sensing logic is configured to generate the event signals when a difference between a first logarithm of a first intensity of a first imaging signal exceeds a second logarithm of a second intensity of a second imaging signal by a threshold value, the first imaging signal and the second imaging signal being generated by a same imaging pixel of the imaging pixels at different times.

5 . The image sensor of claim 1 , wherein the time period is less than 10 microseconds.

6 . The image sensor of claim 1 , wherein the spatial concentration of the event signals in the ROI within the time period is greater than 20 percent of the imaging pixels generating the event signals within the time period.

7 . The image sensor of claim 1 , wherein the event-sensing logic is disposed on a second layer of the imaging sensor disposed between a third layer of the imaging sensor that includes the ROI logic.

8 . A computer-implemented method comprising:
generating imaging signals in response to image light; generating event signals in response to receive the imaging signals from imaging pixels; identifying a Region of Interest (ROI) of the imaging pixels from a spatial concentration of event signals in the ROI of imaging pixels within a time period; and driving an ROI portion of the imaging pixels in the ROI to capture an ROI image frame.

9 . The computer-implemented method of claim 8 , wherein the event signals are generated asynchronously, and wherein the ROI image frame is captured with a global shutter or rolling shutter of the ROI portion of the imaging pixels.

10 . The computer-implemented method of claim 8 , wherein the imaging pixels are configured to sense near-infrared light and reject visible light.

11 . The computer-implemented method of claim 8 , wherein the event signals are generated by event-sensing logic, wherein the event-sensing logic is configured to generate the event signals when a difference between a first logarithm of a first intensity of a first imaging signal exceeds a second logarithm of a second intensity of a second imaging signal by a threshold value, the first imaging signal and the second imaging signal being generated by a same imaging pixel of the imaging pixels at different times.

12 . The computer-implemented method of claim 8 , wherein the time period is less than 10 microseconds.

13 . The computer-implemented method of claim 8 , wherein the spatial concentration of the event signals in the ROI within the time period is greater than 20 percent of the imaging pixels generating the event signals within the time period.

14 . A computer-implemented method comprising:
illuminating an eyebox region with a fringe illumination pattern; capturing a first image of the eyebox region at a first time period while the eyebox region is illuminated with the fringe illumination pattern; capturing a second image of the eyebox region at a second time period while the eyebox region is illuminated with the fringe illumination pattern; generating intensity difference data between the first image and the second image; and identifying an eye feature based at least in part on the intensity difference data.

15 . The computer-implemented method of claim 14 , wherein generating the intensity difference data includes subtracting second pixel rows of the second image from first pixel rows of the first image,
and wherein identifying the eye feature includes identifying a pupil or an iris occupying the eyebox region from an intensity change peak from the intensity difference data.

16 . The computer-implemented method of claim 14 , wherein the fringe illumination pattern is near-infrared light.

17 . The computer-implemented method of claim 14 , wherein a steep drop in light intensity in the second image indicates a position of a pupil in the eyebox region.

18 . The computer-implemented method of claim 14 , wherein the first image is a first light intensity plot along a first scan line in the eyebox region, and wherein the second image is a second light intensity plot along a second scan line in the eyebox region.

19 . The computer-implemented method of claim 14 , wherein the fringe illumination pattern includes bright fringe strips alternating with dark fringe strips.

20 . The computer-implemented method of claim 14 , wherein the second time period is subsequent to the first time period.

Assignees

• Meta Platforms Tech Llc

Inventors

• Chao Qing
• Cavin Robert Dale
• Lestoquoy Guillaume
• Daraeihajitooei Mohammadhossein
• Liu Xinqiao
• Capoccia Raffaele
• Hallstein Sascha
• Li Ziyun

Classifications

• H04N5/33

  Transforming infrared radiation (cameras or camera modules for generating image signals from infrared radiation H04N23/20; circuitry of SSIS for transforming infrared radiation into image signals H04N25/20) · CPC title

• G06V10/25

  Determination of region of interest [ROI] or a volume of interest [VOI] · CPC title

• G06T2207/30201

  Face · CPC title

• G06V10/60

  relating to illumination properties, e.g. using a reflectance or lighting model · CPC title

• G06T2207/10048

  Infrared image · CPC title