emotions-recognition-retail-0003#

Use Case and High-Level Description#

Fully convolutional network for recognition of five emotions (‘neutral’, ‘happy’, ‘sad’, ‘surprise’, ‘anger’).

Validation Dataset#

For the metrics evaluation, the validation part of the AffectNet dataset is used. A subset with only the images containing five aforementioned emotions is chosen. The total amount of the images used in validation is 2,500.

Example#

Input Image

Result

Happiness

Specification#

Metric

Value

Input face orientation

Frontal

Rotation in-plane

±15˚

Rotation out-of-plane

Yaw: ±15˚ / Pitch: ±15˚

Min object width

64 pixels

GFlops

0.126

MParams

2.483

Source framework

Caffe*

Accuracy#

Metric

Value

Accuracy

70.20%

Inputs#

Image, name: data, shape: 1, 3, 64, 64 in 1, C, H, W format, where:

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs#

Name: prob_emotion, shape: 1, 5, 1, 1 - Softmax output across five emotions (0 - ‘neutral’, 1 - ‘happy’, 2 - ‘sad’, 3 - ‘surprise’, 4 - ‘anger’).

Demo usage#

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: