## Use Case and High-Level Description¶

This is a custom-architecture convolutional neural network for 35 facial landmarks estimation.

## Example and Landmarks Definition¶

[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.

Metric

Value

GFlops

0.042

MParams

4.595

Source framework

Caffe*

## Validation Dataset¶

A 1000-sample random subset of a large internal dataset containing images of 300 people with different facial expressions.

## Validation Results¶

The quality of landmarks’ positions prediction is evaluated through the use of Normed Error (NE). The error for the i th 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 k th landmark of the i th sample, and d i is the interocular distance for the i th sample.

Dataset

Mean NE

90 Percentile NE

Standard deviation of NE

Internal dataset

0.106

0.143

0.038

## Inputs¶

Image, name: data, shape: 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

## Outputs¶

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).

## Use Case and High-Level Description¶

This is a custom-architecture convolutional neural network for 35 facial landmarks estimation.

## Example and Landmarks Definition¶

[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.

Metric

Value

GFlops

0.042

MParams

4.595

Source framework

Caffe*

## Validation Dataset¶

A 1000-sample random subset of a large internal dataset containing images of 300 people with different facial expressions.

## Validation Results¶

The quality of landmarks’ positions prediction is evaluated through the use of Normed Error (NE). The error for the i th 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 k th landmark of the i th sample, and d i is the interocular distance for the i th sample.

Dataset

Mean NE

90 Percentile NE

Standard deviation of NE

Internal dataset

0.106

0.143

0.038

## Inputs¶

Image, name: data, shape: 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

## Outputs¶

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).

## Legal Information¶

[*] Other names and brands may be claimed as the property of others.