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:

Legal Information¶

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

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