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: