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Suggested Term Projects. CSE 666, Fall 2014. Guidelines. The described projects are suggestions; if you have desire, skills or idea to explore alternative topics, you are free to do so.
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Suggested Term Projects CSE 666, Fall 2014
Guidelines • The described projects are suggestions; if you have desire, skills or idea to explore alternative topics, you are free to do so. • Finalize the project selection by October 16; have a 1-2 slide (2-3 minutes) presentation describing the project on that day. • Project reporting: • Final goal: estimate performance of proposed method or present a working demo of the system • Presentation: 20 minute on one of four days of classes(Nov. 20, 25, Dec. 2, 4). Presentation should include some topic background (previous works), description of the approach, preliminary results. • Final report : Dec. 16 ? – research style paper with results (background, previous work, detailed approach description, results, bibliography). • Working in groups: you can do so for large projects, but the work should be clearly split and separate reports from each person will have to be submitted. • Using existing projects, libraries, etc. You have to reference all such uses; the project should contain significant amount of your original work.
Fingerprint Deformations • Skin Deformation Models for Fingerprint Matching • Implement previous methods for correlation matching • J. Li, S. Tulyakov, and V. Govindaraju, "Verifying Fingerprint Match by Local Correlation Methods," in First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. , 2007, pp. 1-5. • J. Li, S. Tulyakov, Z. Zhang, and V. Govindraju, "Fingerprint Matching Using Correlation and Thin-Plate Spline Deformation Model," in IEEE Second International Conference on Biometrics: Theory, Applications and Systems (BTAS 08), 2008. • Incorporate deformation cost into matching scores • Propose and implement other methods of skin deformation, e.g. • R. Cappelli, D. Maio, and D. Maltoni. Modellingplastic distortion in Fingerprint images. In Proc. Int'l Conf. Advances in Pattern Recognition, 2001. • DongjinKwon; Il Dong Yun; Sang-Uk Lee, "A Robust Warping Method for Fingerprint Matching," Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on , vol., no., pp.1,6, 17-22 June 2007
Fingerprint Indexing • Modify existing fingerprint indexing algorithm to use in-memory structures; test performance • Mansukhani, P., Tulyakov, S., Govindaraju, V.: A Framework for Efficient Fingerprint Identification Using a Minutiae Tree. Systems Journal, IEEE 4 (2010) 126-137 • Implement alternative indexing method ; compare performances • B. Bhanu and X. Tan, “Fingerprint indexing based on novel features of • minutiae triplets,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. • 5, pp. 616–622, May 2003. • Cappelli, R.; Ferrara, M.; Maltoni, D., "Fingerprint Indexing Based on Minutia Cylinder-Code," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.33, no.5, pp.1051,1057, May 2011 • Other indexing ideas?
Fingerprint Image Binarization • Implement MRF based approach to fingerprint binarization: • Huaigu Cao; Govindaraju, V., "Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.31, no.7, pp.1184,1194, July 2009 • Kai Cao; Eryun Liu; Jain, AK., "Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine Ridge Structure Dictionary," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.36, no.9, pp.1847,1859, Sept. 2014
Fingerprint Hashing • Implement some fingerprint hashing algorithms and compare their performance with existing methods • Farooq, F., Bolle, R.M., Jea, T.-Y., Ratha, N.A.-R., N.: Anonymous and Revocable Fingerprint Recognition. In: Bolle, R.M. (ed.): Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on (2007) 1-7 • Ferrara, M.; Maltoni, D.; Cappelli, R., "Noninvertible Minutia Cylinder-Code Representation," Information Forensics and Security, IEEE Transactions on , vol.7, no.6, pp.1727,1737, Dec. 2012
Face Tracking • Improve face tracking accuracy by performing direction dependent recognition of face parts • Need to re-train algorithms (SVM) performing recognition • Data for training can be collected automatically based on currently recognized positions of recognized patches. Some data verification might be needed. • Improve face tracking accuracy by utilizing non-rectangular region recognition • Improve face tracking speed by utilizing alternative features (e.g. LBP instead of HOG), feature reduction methods, SVM replacement? • Improving face tracking algorithm by adding user-specific matching • Dalal, N.; Triggs, B.; , "Histograms of oriented gradients for human detection," Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on , vol.1, no., pp.886-893 vol. 1, 25-25 June 2005 • M. Isard and A. Blake, “ICONDENSATION- Unifying low-level and high-level tracking in a stochastic framework,” Proceedings of European Conference on Computer Vision, Vol. 1, pp. 893-908, 1998.
Facial Expression Matching • Automated facial expression analysis : • Given positions of eyes, brows and mouth in videos • Detect head movements (e.g. speed in movements, direction, etc.) and investigate if they can be used for person identification • Detect facial expressions from the changes in features extracted near landmark points • Explore if sequences of changes can be used for person identification • S. Tulyakov, T. Slowe, Z. Zhang, and V. Govindaraju, "Facial Expression Biometrics Using Tracker Displacement Features," in Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, 2007, pp. 1-5. • A. L. Kashyap, S. Tulyakov, and V. Govindaraju, "Facial Behavior as a Soft Biometric," in International Conference on Biometrics (ICB 2012), 2012.
Multi-frame face matcher score fusion • Match subsequent frames in the video to a single enrolled face image • Need to effectively fuse these scores • Use difference between faces in video frames • Goal: automatically learnable fusion method • S. P. Satheesan, S. Tulyakov, and V. Govindaraju, "A feature information based approach for enhancing score-level fusion in multi-sample biometric systems," in Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference on, 2013, pp. 1-4. • X. Cheng, S. Tulyakov, and V. Govindaraju, "Utilization of matching score vector similarity measures in biometric systems," in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, 2012, pp. 111-116.