- What did you personally accomplish this week on the project?
Since I got back from spring break I have been working on optimizing my facial detection. In order to increase the accuracy, I changed the dataset used for training and increased the the total number of training examples (1500 positive and 2000 negative). I also made some alterations to the adaboost algorithm. After making these changes, I am getting 88% accuracy for Faces, and 90% accuracy for Non-faces. This is only when detecting a single face in a 24×24 image. I am using a test set of 500 positive and 500 negative images.
Next, I was working on the sliding window code to detect multiple faces in an image. This is the sample output:
There are multiple overlapping boxes, which is expected. I used Non-maximum suppression to merge overlapping boxes into one. The final output is as follows:
As you can see, there is a high false-positve rate. Additionally, not all the faces were detected. I am currently trying to improve the detection. I will try improve my training set, and use more examples. In addition, I will use more features (currently, I only select 100). The issue with these changes however, is that this will make training much slower.
- Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
I think I am slightly behind schedule, as I was hoping to be completely done with detection by this week. This is mostly because I didn’t manage to get much done during spring break. However, I think I can catch up this following week.
- What deliverables do you hope to complete in the next week?
I will continue to optimize to get better accuracy and reduce the number of false-positives. I will also integrate my code into the MVP script.