The course project this semester will involve you reading at least 5 research paper and presenting them to the class. At the end, you will produce a report of at least 3 pages and give a 20 minutes presentation to the class about the papers you selected.
The 5 papers are:
• Grgic, Mislav; Delac, Kresimir; Grgic, Sonja (2011). "SCface–surveillance cameras face database". Multimedia Tools and Applications. 51 (3): 863–879
• Wallace, Roy, et al. "Inter-session variability modelling and joint factor analysis for face authentication." Biometrics (IJCB), 2011 International Joint Conference on. IEEE, 2011
• Wolf, Lior, Tal Hassner, and Itay Maoz. "Face recognition in unconstrained videos with matched background similarity." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011
• Ng, Hong-Wei, and Stefan Winkler. "A data-driven approach to cleaning large face datasets." Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014
• "IMDB-WIKI". data.vision.ee.ethz.ch. Retrieved 2018-03-13
You should produce 3 pages report with your findings. It should be A4, size 11 points Arial font with 1 inch margin. 3 pages are not very long, so you need to be strategic about what you present.
Note: When writing your report, you may be tempted to cut and paste pieces of various online sources together into a report or even just parts of your report. For this project, you are NOT permitted to reuse material from ANY other source in ANY quantity.
These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice. Unethical use is strictly forbidden.DESCRIPTION:
There are multiple methods or multiple ways in facial recognition system but in general way, face recognition work on face, facial expression, and facial features from original image with different faces within a large face datasets One of the most important methods for the face recognition is large face datasets But this is very difficult to build because it is time consuming and also requires lots of work for processing of generic raw data. The person’s face is identified by analyzing features based on the exclusive facial texture, face shape, face pattern. So it is quite important to build large face datasets, so that the algorithm starts detecting image faces from original input image.
In this paper the author derives a new method which utilizes large face datasets in face recognition to help process the structure of faces. Sets of facial images are obtained by Face Detection or Outlier Detection. Any non recognized faces found in image by the face detector or another person present in the same image is finally considered as an unknown person.Ultimately, the main goal is to remove the outliners from the detected...
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Solution.docx and Solution.pptx.