emotion-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
emotions-recognition-retail-0003.jpg
Happiness

Specification

Metric Value
Input face orientationFrontal
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%

Performance

Inputs

  1. name: "input" , shape: [1x3x64x64] - An input image in [1xCxHxW] format. Expected color order is BGR.

Outputs

  1. name: "prob", shape: [1, 5, 1, 1] - Softmax output across five emotions ('neutral', 'happy', 'sad', 'surprise', 'anger').

Legal Information

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