README.md

age-gender-recognition-retail-0013

Use Case and High-Level Description

Fully convolutional network for simultaneous Age/Gender recognition. The network is able to recognize age of people in [18, 75] years old range, it is not applicable for children since their faces were not in the training set.

Validation Dataset - Internal

~20,000 unique subjects representing diverse ages, genders, and ethnicities.

Example

Input Image	Result
	Female, 18.97
	Male, 26.52
	Male, 33.41

Specification

Metric	Value
Rotation in-plane	±45˚
Rotation out-of-plane	Yaw: ±45˚ / Pitch: ±45˚
Min object width	62 pixels
GFlops	0.094
MParams	2.138
Source framework	Caffe*

Accuracy

Metric	Value
Avg. age error	6.99 years
Gender accuracy	95.80%

Inputs

Image, name: data, shape: 1, 3, 62, 62 in 1, C, H, W format, where:

• C - number of channels
• H - image height
• W - image width

Expected color order is BGR.

Outputs

1. Name: fc3_a, shape: 1, 1, 1, 1 - Estimated age divided by 100.
2. Name: prob, shape: 1, 2, 1, 1 - Softmax output across 2 type classes [0 - female, 1 - male].

Demo usage

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

• Interactive Face Detection C++ Demo
• G-API Interactive Face Detection Demo

Legal Information

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