Neural network for eye image segmentation and image quality estimation

1. A method for determining eye contours in a semantically segmented eye image, the method is under control of a hardware processor and comprises:

receiving a semantically segmented eye image of an eye image comprising a plurality of pixels, wherein a pixel of the semantically segmented eye image has a color value,

wherein the color value of the pixel of the semantically segmented eye image is a first color value, a second color value, a third color value, or a fourth color value,

wherein the first color value corresponds to a background of the eye image,

wherein the second color value corresponds to a sclera of the eye in the eye image,

wherein the third color value corresponds to an iris of the eye in the eye image, and

wherein the fourth color value corresponds to a pupil of the eye in the eye image;

determining a pupil contour using the semantically segmented eye image; and

determining an iris contour using the semantically segmented eye image,

wherein,

the pupil contour is determined using a first binary image created based on the semantically segmented eye image, wherein a color value of a first binary image pixel of the first binary image is the fourth color value or the third color value, and/or

the iris contour is determined using a second binary image created based on the semantically segmented eye image, wherein a color value of a second binary image pixel of the second binary image is the third color value or the second color value.

2. The method of claim 1 , wherein the first color value is greater than the second color value, wherein the second color value is greater than the third color value, and wherein the third color value is greater than the fourth color value.

3. The method of claim 1 , wherein determining the pupil contour using the semantically segmented eye image comprises:

determining a pupil contour border;

removing a plurality of pixels from the pupil contour border; and

determining the pupil contour as an ellipse from remaining pixels of the pupil contour border.

4. The method of claim 3 wherein determining a pupil contour border further comprises:

determining contours in the first binary image; and

selecting a longest contour of the determined contours in the first binary image as a pupil contour border.

5. The method of claim 4 , wherein the color value of the first binary image pixel of the first binary image is the fourth color value if a corresponding pixel in the semantically segmented eye image has a value greater than or equal to the fourth color value, and the third color value if the corresponding pixel in the semantically segmented eye image has a value not greater than or equal to the fourth color value.

6. The method of claim 4 , comprising:

determining a pupil contour points bounding box enclosing the pupil contour border;

computing a pupil points area size as a diagonal of the pupil contours points bounding box; and

determining a pupil contour threshold based on the pupil points area size.

7. The method of claim 6 , wherein the pupil contour threshold is a fraction multiplied by the pupil points area size, and wherein the fraction is in a range from 0.02 to 0.20.

8. The method of claim 3 , comprising creating a third binary image comprising a plurality of pixels, wherein a color value of a third binary image pixel of the plurality of pixels of the third binary image is the third color value or the second color value.

9. The method of claim 8 , wherein the color value of the third binary image pixel of the plurality of pixels of the third binary image is the third color value if a corresponding pixel in the semantically segmented eye image has a value greater than or equal to the third color value, and the second color value if the corresponding pixel in the semantically segmented eye image has a value not greater than or equal to the third color value.

10. The method of claim 8 , wherein removing a plurality of pixels from the pupil contour border comprises, for a pupil contour border pixel of the pupil contour border:

determining a closest pixel in the third binary image that has a color value of the second color value and that is closest to the pupil contour border pixel;

determining a distance between the pupil contour border pixel and the closest pixel in the third binary image; and

removing the pupil contour border pixel from the pupil contour border if the distance between the pupil contour border pixel and the closest pixel in the third binary image is smaller than a pupil contour threshold.

11. The method of claim 1 , wherein determining the iris contour using the semantically segmented eye image comprises:

determining an iris contour border;

removing a plurality of pixels from the iris contour border; and

determining the iris contour as an ellipse from remaining pixels of the iris contour border.

12. The method claim 11 , wherein determining the iris contour border comprises:

determining contours in the second binary image; and

selecting a longest contour of the determined contours in the second binary image as an iris contour border.

13. The method of claim 12 comprising:

determining an iris contour points bounding box enclosing the iris contour border;

computing an iris points area size as a diagonal of the iris contours points bounding box; and

determining an iris contour threshold based on the iris points area size.

14. The method of claim 11 , wherein a color value of the second binary image pixel of the plurality of pixels of the second binary image is the third color value if a corresponding pixel in the semantically segmented eye image has a value greater than or equal to the third color value, and the second color value if the corresponding pixel in the semantically segmented eye image has a value not greater than or equal to the third color value.

15. The method of claim 14 , wherein the iris contour threshold is a fraction multiple by the iris points area size, and wherein the fraction is in a range from 0.02 to 0.20.

16. The method of claim 11 comprising creating a fourth binary image comprising a plurality of pixels, wherein a color value of a fourth binary image pixel of the plurality of pixels of the fourth binary image is the second color value or the first color value.

17. The method of claim 16 , wherein a color value of a fourth binary image pixel of the plurality of pixels of the fourth binary image is the second color value if a corresponding pixel in the semantically segmented eye image has a value greater than or equal to the second color value, and the first color value if the corresponding pixel in the semantically segmented eye image has a value not greater than or equal to the second color value.

18. The method of claim 16 , wherein removing a plurality of pixels from the iris contour border comprises, for an iris contour border pixel of the contour border:

determining a closest pixel in the fourth binary image that has a color value of the first color value and that is closest to the iris contour border pixel;

determining a distance between the iris contour border pixel and the closest pixel in the fourth binary image; and

removing the iris contour border pixel from the iris contour border if the distance between the iris contour border pixel and the closest pixel in the fourth binary image is smaller than an iris contour threshold.

19. The method of claim 1 comprising determining a binary mask to cover an irrelevant area in the semantically segmented eye image.

20. The method of claim 19 wherein determining the binary mask to cover the irrelevant area in the eye image comprises:

creating a binary mask image comprising a plurality of pixels, wherein a binary mask image pixel of the binary mask image has a color value;

setting the color value of the binary mask image pixel to the third color value if a corresponding pixel in the semantically segmented eye image has a value greater than or equal to the third color value; and

setting the color value of the binary mask image pixel to the second color value if a corresponding pixel in the semantically segmented eye image has a value not greater than or equal to the third color value.

21. The method of claim 19 wherein the irrelevant area comprises a portion of the semantically segmented eye image outside of an area defined by the iris contour.

22. The method of claim 19 , comprising:

applying the binary mask to the semantically segmented eye image to generate a relevant eye image comprising a portion of the eye image that excludes the irrelevant area; and

calculating a biometric signature from the relevant eye image.

23. The method of claim 22 , wherein the biometric signature comprises an iris code.

24. The method of claim 1 , further comprising creating a polar image of an iris of an eye in the eye image from the eye image using the pupil contour, the iris contour, and the mask for the irrelevant area in the semantically segmented eye image.

25. The method of claim 1 , wherein receiving the semantically segmented eye image of an eye image comprising a plurality of pixels comprises:

receiving an eye image;

processing the eye image using a convolution neural network to generate the semantically segmented eye image; and

processing the eye image using the convolution neural network to generate a quality estimation of the eye image,

wherein the convolution neural network comprises a segmentation tower and a quality estimation tower, wherein the segmentation tower comprises segmentation layers and shared layers,

wherein the quality estimation tower comprises quality estimation layers and the shared layers,

wherein a first output layer of the shared layers is connected to a first input layer of the segmentation tower and a second input layer of the segmentation tower,

wherein the first output layer of the shared layers is connected to an input layer of the quality estimation layer, and

wherein receiving the eye image comprises receiving the eye image by an input layer of the shared layers.