My project has really started getting going this week. After learning more about Random Forests and Support Vector Machines, I've started running models and finally getting results. I still feel I'm missing a lot of the advanced tools which would really set this work apart, and that's frustrating. I know that for all the work I do in feature extraction and model creation, there are newer techniques which would outperform the things I'm trying. I'm getting helpful guidance from others in the lab and learning so much, but in two months I don't know how I could be familiar with the state of the art in all relevant fields.
Skills gaps notwithstanding, this has been a very good week in the lab. Seeing results come in is great and the immediacy of feedback is encouraging. After changing a few parameters in a model, I get results in a few minutes on how they perform and can continue tuning from there. I really hope to find an interesting modeling method from this.
Support from the lab has been great. Each week I give a presentation to my advisor and some grad students who critique the content and the presentation itself which is usually more valuable. Most of the time I'm focused on content, but in those presentations, I understand how important a good pitch is. After working for the full 9 weeks, I'm sure the final presentation will go very smoothly.
This week I looked more closely at local fingerprint patterns and begin building a model to predict the authenticity of fingerprints considering their textures. I gave a presentation on the topic to my mentor and some grad students and their feedback was very helpful. They gave guidance on the little things like including slide number to more substantive issues about explaining the problem clearly. Overall, I learned a lot from putting together the presentation and from their feedback. Next week I'll move on with our model and optimize the features we use in making predictions. First, I'll need to read a lot more documentation on MATLAB random forest models.
We also took part in a few seminars and activities. I appreciated hearing from the Associate Dean of graduate studies who gave guidance on applying to grad school. I also liked the low ropes course that we took part in today with the chemistry REU students.
Looking forward to more in the weeks ahead!
Two months is an interesting timeframe to work on a research project. While it's hard to see a project from start to finish in that time, it is long enough, I hope, to be immersed in a project and experience a research environment. This week I started that process by getting up to speed on my project. To improve interoperability of fingerprint recognition systems - that is how well fingerprints acquired on one scanner can be used by a different scanner - we are considering a number of features not previously employed for fingerprint analysis including a local descriptor of texture in an image known as local phase quantization (LBQ). I've spent most of my working days so far reading articles on fingerprint authentication, Fourier transformations, and local image descriptors. Each new concept has sent me searching for a more complex understanding of the topic in hopes of understanding the mechanisms at work. Today (6/2), I spent the day summarizing a paper on LBQ which has been a very challenging way to ensure I truly understand the descriptor. Even in this short time, I have made measurable progress in my understanding of a complex concept and appreciate the exercise of summarizing a challenging paper. Next week we'll be implementing code to extract this features and others and using them to train a discriminator. I'm looking forward to moving on with the project in that way.