CSE 4392/6367 - Project

Due date: Wednesday, May 13, 11:59pm.

Mandatory weekly progress reports are due every Monday at 11:59pm, starting Monday 04/20, and ending Monday 05/11.

The project constitutes 25% of the course grade. Students who have not already obtained the instructors consent on a different project must do the project specified here. After April 13, weekly progress reports by all students will be due weekly (by e-mail to the instructor and the GTAs), each Monday at 11:59pm, until the project is submitted.

Task

Implement a face detector that is trained using AdaBoost, combines information from skin color and rectangle filters, and utilizes the ideas of bootstrapping and classifier cascades. In particular, the following methods/concepts must be utilized in this face detector:

Data

All the data that you need from this project can be downloaded from zipped file training_test_data.zip, which has size 47MB. You can access individual files from directory training_test_data, When you train your system, you can use windows from images in directory training_faces as positive examples. As negative examples, you can use windows from images in directory training_nonfaces. For bootstrapping, once you have trained a detector, you should apply it to all images in training_faces and training_nonfaces, identify windows where the detector makes mistakes, add those windows to the training set, and retrain.

As test data, you can use the images in directories test_cropped_faces, test_face_photos, and test_nonfaces. You are not allowed to use any of these test data for training.

Grading

Grading will be based on how well you applied knowledge that you obtained in this course in order to design a detector that is accurate and efficient. For CSE 6367, 22.5% of the project grade will be assigned to each of the four components of the system (AdaBoost, skin detection, bootstrapping, cascades). Another 10% will be based on the four weekly progress reports, and will consider timeliness of submission, quality of description, and evidence of progress.

For CSE 4392, 30% of the project grade will be assigned to each of the three mandatory components of the system (AdaBoost, skin detection, bootstrapping), and 10% will be based on the four weekly progress reports. Implementing classifier cascades is worth 5% extra credit.

In addition to correctness and quality of implementation, you will also be graded based on the decisions and choices you make in building your system. You will have to make several decisions, including:

In general, this project is intended to be a simulation of a project that you could be assigned when working in the real world. In such projects, much less is specified that in a typical homework assignment; the system designer needs to evaluate different choices at each step, and finally make choices that lead to a good product/system. During this course you have learned a variety of different computer vision methods, and you have also encountered several different approaches for making system design choices and for justifying those choices. This is an opportunity to use what you have learned.

Presentations

Students will need to present their project implementations, in a 5-10 minute presentation, for which slides should be prepared. Presentation times will be during finals week, and will be arranged by the instructor. The presentations should specify the main choices that were made in designing the system, and the accuracy/efficiency of the results that were obtained on the test data.