Eyelid detection

Description MAM:
to appear soon

Description WACV2017:
to appear soon

Description PETMEI 2016:
Let I[r; c] be a digital close-up image of the eye in the nearinfrared spectrum with r rows and c columns. The eyelid detection task consists of selecting two sets of pixels Pl and Pu in I that lie respectively on the lower and upper eyelids, which are then used to fit functions representing the outline of each eyelid.
The proposed method consists mainly of I) rescaling the image preserving dark regions to reduce noise and computation costs, II) filtering the image according to a combination of local features to generate a likelihood map for the eyelids, III) detecting edges on the likelihood map, and selecting two edges to represent the eyelids based on their orientation and horizontal shift in respect to one another, enclosed intensity value, and accumulated likelihood. These steps are described in detail in the following subsections, followed by a graphical representation exemplifying the output of each stage in the algorithm.

Results eyelid detection:

Outline similarity with the jaccard index(higher is better).

Eyelid aperture estimation (lower is better).

Cumulative detection rate (top and left is better).

Download Paper:
WACV 2017 (on permission)

Sourcecode and lib downloads:

  • Automatic annotation MAM
  • Eyelid detection WACV 2017
  • Eyelid detection PETMEI 2016
All algorithms (ftp)

Data set downloads:

  • Eyelid detection data set WACV 2017
  • Eyelid detection data set PETMEI 2016
All data sets (ftp)