Project EESM5547: Face recognition and average face!! • Prerequisites: Gather a face database of the class (Let’s do it within the next week)! • At least, each student takes a picture with front and centred view of your face! • Each student provides his face image from different orientations and variation of expression and wearings etc.! • The pictures can be taken with slightly variant illuminance conditions or blurring as well! • Each student provides 15 portraits! • Send it to the TA by next weekend! • You can do the project in group of two or per-student. In your final report and presentation, the contributions need to be specified to individual. The two-student projects need to show significantly larger work package (e.g. include other methods) or much deeper analysis to the result. Grades will be provided per individual. If you are a UG, you can choose to do it with others in a group.! ! Project definition Face detection and recognition with comparative studies! ! GENERAL REQUIREMENT:! From this project, the following key techniques should be included in the procedure:! * Image enhancement in either spatial or frequent domain! * Restore distortion when necessary! * Face detection / recognition! * Feature Recognition! General Guidelines and minimum work packages ! MODELLING! Use the Course-dataset (preferred, since you worked on it together) or the one in http://www.cl.cam.ac.uk/research/dtg/ attarchive/facedatabase.html to train your model. Remember to divide them into training and testing dataset. Consider the following questions to help:! (a) How should you represent you image as inputs for PCA? How about using other methods, such as keypoint-based methods?! (b) How do the (leading) eigenfaces look like as an image (show some examples in your report)? What do they mean?! (c) How does the importance of the eigenfaces decrease?! (d) When keypoint-based methods are used, what are the limitations (e.g. repetitive features)? How to reduce the side- effect caused by them? ! ! PREPROCESSING! Before doing anything else, try to enhance the visibility of the datasets using learnt techniques. Use examples to illustrate the improvement.! ! RECONSTRUCTION! Calculate the most average look of the class. (In order to get a perfect results, probably you want to rotate and shift the image manually a bit to align the eye positions. These operations are not required in your results, but it would be nice to have.) Consider the following questions to help your project:! (a) Observe the difference between reconstructed and original images, as the number of eigenfaces used in reconstruction increases.! (b) How many eigenfaces are required to recover an original face with reasonable errors?! (c) Does the number of needed eigenfaces change from person to person, or not?! (d) How do you select the co-efficiencies to get an “average”-look?! ! RECOGNITION! Use the images from the testing dataset to demonstrate your face recognition statistically. As comparison, please take 20 additional arbitrary images (not face, or faces of animals or cartoon), show the recognition results comparatively. Also, please discussion over the results and give your justification.! ! IDENTIFICATION! Using another subset of your testing data, or take several other faces from you and friends to identify who is the guy in the picture. Show your results statistically. (remember to use false samples as well.) Consider the following hints:! (a) Use the same training and testing sets as above! (b) Develop your method base on what you learn from image features. If you method is able to tell that a new face is known, how does it continue to tell which face in the training set it corresponds to?! (c) The following plot shows a general flow. You can use your own as well.! ! Grading – Content: 50%! – Report: 30%! – Presentation: 20%! Extension of work packages will be rewarded and strongly encouraged! Coding language other than Matlab is encouraged! !
