Insight AI

1. Artificial Face Aging
Markus B. Huber
Insight AI, Spring 2017

2. Motivation
Problem:
To design a model that renders natural face aging without image
pairs.
Applications:
Automatic face aging for cross-age face recognition.
Now + 20 years- 20 years

3. Scope
To learn a representation of face aging.
To generate artificial images of face aging for a
set age groups.

4. Image Data
Properties:
- Face image with known age.
- Age distribution.
Source:
IMDB-Wiki data set with celebrities’ images:
500k+
Experiments:
- Compared different level pre-processing.
- Compared two image sizes.

5. Image Data Pre-Processing
Pre-processing:
- Data cleaning, face detection, landmark
alignment, cropping, resampling.
- Final data set: ~125k images.
- 6 age groups.

6. Approach: GAN
Unsupervised Learning:
- Autoencoder
- Generative Adversarial Networks (GANs)
Source: OpenAI

7. Approach: GAN
Unsupervised Learning:
- Autoencoder
- Generative Adversarial Networks (GANs)
Source: OpenAI

8. Approach: AC-GAN
Semi-supervised learning GANs:
- Use deep convolutional GANs (DC-GAN).
- Add class labels for images.
- Auxiliary contrained GAN (AC-GAN)
Experiments:
Compare different parameters:

9. Approach
AC-GAN
Real
Image
Data
Artificial
Images
Age
Classifier
D
G
Experiments:
Compare different parameters:

10. Results
0-19 20-29 30-39 40-49 50-59 60+Age= 0-19 20-29 30-39 40-49 50-59 60+Age=

11. Results
0-19 20-29 30-39 40-49 50-59 60+Age= 0-19 20-29 30-39 40-49 50-59 60+Age=

12. Experiments
Data:
Image quality depends on pre-processing and data cleaning.
GAN models:
- W-GAN produces better results.
- Model parameter.

13. Summary
Summary:
GAN model learned a representation of face aging.
Challenges:
- Image quality depends on pre-processing and data cleaning.
- Model tuning is challenging.
Blog post:
www.github.com/mbhuber/Insight-AI-Project

14. About me
Electronics Research Lab
Volkswagen Group of America