• Completed by: Hamdi Alperen Çetin & Emre Doğan

• We propose a Convolutional Neural Network Model to succesfully estimate the age of a person given her/his cropped face image. Different from the classical CNN models, our model ends up with a regression layer, not a classifier one. So, backpropagation process is done based on the regression output.

• For a more detailed technical report, check here.

• We trained our model with a downsampled version of UTKFace Dataset.
• Due to its large size, we cannot share original dataset(all training + validation + test data). But you can find some samples of our dataset from here. Notice that the first 3 letters of any image corresponds to its output layer (age of the person in the image).
• Not to spend time on reading data on each execution, we converted our training, validation and test data into .npy format by read_data.py.

• Our architecture can be seen in the figure above. It consists of several consecutive convolution layers. Another important point regarding the model is that instead of a classifier approach, we used a regression based model so that backpropagation flow starts from some continuous age value.

To see a more detailed tensorboard graph regarding our model, click here.

• To decide on hyperparameters, we tried many different scenarios. Training and validation losses (Mean Average Error) for each scenario can be found here.

• The best results are taken when the hyperparameters are,

• The corresponding results in our best model is given below,