Supervisor: Martin Kampel
Automatically detecting the gender of a person or estimating his/her age is valuable in many fields of work.
In the area of computer vision exist a variety of possible approaches, mostly based on machine learning, to accomplish this task.
A higher complexity arises due to the claim to classify in-the-wild images, i.e. pictures which do not fulfill certain restrictions, usually found under laboratory conditions.
Not fulfilled conditions for in-the-wild images could be (without any claim to completeness):
- 0-n faces visible
- partially occluded faces
- faces in non-front view
The goal of this work is to train and evaluate a given convolutional neural network  to accomplish gender classification and age estimation on images found in the wild (e.g. web images). As a starting point, the CNN proposed by Hassner et al.  should be trained and evaluated.
This comprises certain tasks as finding and processing a suitable dataset for training, evaluating and testing the network, as well as tuning the hyperparameters, and evaluating the solution.
- Literature Review – CCNs in general, Levi/Hassner, VGG-16, or other feasible solutions
- Dataset research and processing
- Preprocessing of the images
- Recurrent Training and Evaluation
- Final Evaluation
- Written Report/Thesis and final presentation
- Experience with python (NumPy, torch7, …)
- Goal-oriented working method
- Machine learning knowledge
- Scientific writing
- Deep learning knowledge beneficial (CNN, keras, theano, tensorflow, etc.)
 Levi, Gil, and Tal Hassner. “Age and gender classification using convolutional neural networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2015.