Final Result

Finally we could complete the project. The results were no that great. 67% of the faces could be detected with good number of false positives. The most important thing is that we could understand the difficulty in constructing the database. We did not expect database creation to be that cumbersome. So, we could understand the practical hurdles in solving a real-world problem using machine learning methods.

Today's update

Today we studied how to use the neural network toolbox. We tried with different training data. We experimented with four(20*20) face images and soon realized that 150 images that we have is not at all sufficient for training/validation/testing. So, we again started to collect training data. Till now we are working only on (20*20) image. Next we plan on working 1. On images of all sizes 2. Create our own code for neural networks(instead of using toolbox);

At last Database is ready

Since 2-3 days, Srikanth Parupati and I have been working on creating a database of face/non-face images(of 20*20 size). The database consists of ~200 images. Now we are learning to use the Neural Network Toolbox provided by Matlab.