This is a custom-architecture convolutional neural network for 35 facial landmarks estimation.
[Left Eye] p0, p1: corners of the eye, located on the boundary of the eyeball and the eyelid.
[Right Eye] p2, p3: corners of the eye, located on the boundary of the eyeball and the eyelid.
[Nose] p4: nose-tip point; p5: lowest point of the nasal septum; p6, p7: right-bottom and left-bottom of the nose wing.
[Mouth] p8, p9: mouth corners on the outer boundary of the lip; p10, p11: center points along the outer boundary of the lip.
[Left Eyebrow] p12: starting point of the upper boundary of the eyebrow; p13: mid-point of the upper arc of the eyebrow; p14: ending point of the upper boundary of the eyebrow.
[Right Eyebrow] p15: starting point of the upper boundary of the eyebrow; p16: mid-point of the upper arc of the eyebrow; p17: ending point of the upper boundary of the eyebrow.
[Face Contour] p26: chin center; p18, p34: upper points of the face contour aligned with the outer corners of the eyes; p19~p25: boundary points, evenly distributed along the curve p18-p26; p27~p33: boundary points, evenly distributed along the curve p26-p34.
A 1000-sample random subset of a large internal dataset containing images of 300 people with different facial expressions.
The quality of landmarks' positions prediction is evaluated through the use of Normed Error (NE). The error for the ith sample has the form:
where N is the number of landmarks, p-hat and p are, correspondingly, the prediction and ground truth vectors of the kth landmark of the ith sample, and di is the interocular distance for the ith sample.
|Dataset||Mean NE||90th Percentile NE||Standard deviation of NE|
1, 3, 60, 60 in the format
B, C, H, W, where:
B- batch size
C- number of channels
H- image height
W- image width
The net outputs a blob
align_fc3 with the shape:
1, 70, containing row-vector of 70 floating point values for 35 landmarks' normed coordinates in the form (x0, y0, x1, y1, ..., x34, y34).
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