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').

Legal Information

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