CMSC828B Advanced Topics in Information Processing: Recent Advances in Biometrics

General Information



Class Time  

Mon., Wed: 3:30-4:45


CSI 3120

Course Info

See below


Readings available in handouts and on web.  See below 






David Jacobs



djacobs at cs dot umd dot edu



AVW 4421


Office hours

Wed 1:00-2:00 or by appt. (or just drop by).





In biometrics we study methods for identifying organisms (usually people) from measurements of their characteristics.  In this class we will read recent papers in order to understand the state of the art in biometrics.  Topics considered will be based on student interest, but I expect to have a significant emphasis on face recognition, with a broad range of additional topics.  This course is intended primarily for students with a research interest in biometrics or computer vision, and will not be a core MS or PhD class for CS students.


Possible class topics include:


Here is my current plan for the workload of the class. 

1) Reports.  Prior to class, students will read assigned papers and write a one page report summarizing and critiquing the work. Each student must turn in 14 reports.  Students should not write a report for classes for which they are giving a presentation.

2) Presentation.  Each student will take responsibility for presenting work during one class.  This will include selecting two papers to be read by the class, and presenting this work and related work to the class.  Students currently performing research in biometrics are encouraged to give a presentation about their research. In this case, the student should select two papers by other authors that provide appropriate background to this research.  For detailed expectations of presentation, see this rubric.

3) Project.  Students will implement and experiment with biometric methods, using real-world data sets. 

4) Class Participation.  Everyone should read papers before class and contribute to discussion of them.

Note: visitors or auditors are welcome.  However, if you are attending a class in which we will discuss papers, you should complete a report on one of these papers (see requirement 1).

Class Schedule

Date Presenter Topic Reading
1. 8/30 Jacobs Introduction  
2. 9/1 Jacobs Introduction to Linear Subspaces Eigenfaces for Recognition
M. Turk and A. Pentland

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
PN Belhumeur, JP Hespanha, DJ Kriegman
3. 9/8 Jacobs Introduction to lighting in face recognition ``On the Effect of Illumination and Face Recognition'' by Jeffrey Ho and David Kriegman.  (See me for copy).

``From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,'' PAMI Vol. 23, No. 6 (2001).  A. Georghiades, P. Belhumeur, and D. Kriegman.  (Optional.  This work is summarized in the above book chapter, but this contains more details).

``Lambertian Reflectance and Linear Subspaces,'' IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(2):218-233, (2003).  R. Basri and D. Jacobs. 
4. 9/13 Jacobs Introduction to Local Features ``Local feature analysis: a general statistical theory for object representation,'' Network: computation in neural systems, 1996.  P. Penev and J. Atick.

``Distortion invariant object recognition in the dynamic link architecture,'' PAMI, 1993.  M. Lades, J. Vorbruggen, J. Buhmann, J. Lange, C. v.d. Valsburg, R. Wurtz, and W. Konen.
5. 9/15 Jacobs Introduction to the Psychology of Face Recognition ``Face recognition by humans: nineteen results all computer vision researchers should know about,'' Proceedings of the IEEE, 2006.  P. Sinha, B. Balas, Y. Ostrovsky, and R. Russell.

``Generalization to novel images in upright and inverted faces,'' Perception, 1996.  Y. Moses, S. Ullman and S. Edelman.
6. 9/20 Abhishek Sharma Bayesian Face Recognition ``Probabilistic visual learning for object representation,'' IEEE PAMI 1997, B. Moghaddam and A. Pentland.

``Bayesian Face Recognition Using Support Vector Machine and Face Clustering,'' CVPR 2004.  Z. Li and X. Tang.
7. 9/22 Jonghyun Choi PLS for Face Recognition Overview and Recent Advances in Partial Least Squares (R. Rosipal)

A Robust and Scalable Approach to Face Identication (W. Schwartz, H. Guo and L. Davis)
8. 9/27 Arijit Biswas Security in Biometrics ``Combining cryptography with biometrics effectively,'', University of Cambridge Technical Report, 2005.  F. Hao, R. Anderson, and J. Daugman.

``Biometric encryption,'' Chapter 22 in ICSA guide to Cryptography.  C. Soutar, D. Roberge, A. Stoianov, R. Gilroy, and B. Kumar.
9. 9/29 Huy Tho Ho Pose in Face Recognition 1) X. Zhang and Y. Gao, "Face Recognition across pose: A Review", Pattern Recognition, 2009.

2) V. Blanz and T. Vetter, "Face Recognition Based on Fitting a 3D Morphable Model", IEEE PAMI 2003.
10. 10/4 Ching Lik Teo Infrared Stan Z. Li et al, "Illumination Invariant Face Recognition Using Near Infrared Images", PAMI v29(4), April 2007:

J. Wang et al, "Fusion of Palmprint and Palm Vein Images for Person Recognition  Based on 'Laplacianpalm' Feature", CVPR 2007:
11. 10/6 Sumit Shekhar Sparse representations and dictionaries in Face Recognition Robust Face Recognition via Sparse Representation:

Discriminative K-SVD for dictionary learning in face recognition:
12. 10/11 Anne Jorstad Expression in Face Recognition Recognizing Expression Variant Faces from a Single Sample Image per Class

Face Recognition Using Local Binary Decisions
13. 10/13 Rajiv Jain Signatures/Handwriting
Automatic Writer Identication Using Connected-Component Contours and Edge-Based Features of Upper-Case Western Script

Automatic Signature Verification: The State of the Art
14. 10/18 David Jacobs Fingerprints Fingerprint Matching, by A. Jain, J. Feng and K. Nandakumar

FingerCode: a filterbank for fingerprint representation and matching, by A. Jain, S. Prabhakar, L. Hong and S. Pankanti
15. 10/20 Joao Soares Lighting in Face Recognition Face Recognition from a Single Training Image under Arbitrary Unknown Lighting Using Spherical Harmonics, by Zhang and Samaras. PAMI 2006
Total variation models for variable lighting face recognition, by Chen, Yin, Zhou, Comaniciu, and Huang. PAMI 2006.
16. 10/25 Jonathon Phillips Using Challenge Problems to Advance Biometrics Phillips PJ, Scruggs WT, O’Toole AJ, Flynn PJ, Bowyer KW, Schott CL, Sharpe M.  FRVT 2006 and ICE 2006 large scale results.  IEEE Trans. Pattern Analysis and Machine Intelligence. 2010;32:831-846.

Beveridge, J.R.;   Givens, G.H.;   Phillips, P.J.;   Draper, B.A.;   Yui Man Lui;    Focus on quality, predicting FRVT 2006 performance, 8th IEEE International Conference on Automatic Face & Gesture Recognition, 2008.
17. 10/27 David Jacobs Face Recognition in the Wild Eric Nowak and Frederic Jurie.
Learning visual similarity measures for comparing never seen objects.
Computer Vision and Pattern Recognition (CVPR), 2007.

Yaniv Taigman, Lior Wolf, and Tal Hassner.
Multiple One-Shots for Utilizing Class Label Information.
British Machine Vision Conference (BMVC), 2009.
18. 11/1 David Jacobs Iris Recognition  J. Daugman, “How iris recognition works,” IEEE Trans. Circ. Syst.Video Technol., vol.14, no.1, pp. 21–30, 2004.

J. R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. Zappia, W. Y. Zhao, “Iris on the MoveTM: acquisition of images for iris recognition in less constrained environments”, Proc. IEEE, vol. 94, no. 11, pp. 1936–1946, 2006.
19. 11/3 David Jacobs Attributes Attribute and Simile Classifiers for Face Verification.
International Conference on Computer Vision (ICCV), 2009.
[PDF] from  A Farhadi, I Endres, D Hoiem, D Forsyth - 2009 -
20. 11/8 Sameh Khamis Non-human biometrics Ravela, S.
Shaping Receptive Fields for Affine Invariance
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004)

Martinez-Munoz, G., Zhang, W., Payet, N., Todorovic, S., Larios, N., Yamamuro, A., Lytle, D., Moldenke, A., Mortensen, E., Paasch, R., Shapiro, L., Dietterich, T.
Dictionary-Free Categorization of Very Similar Objects via Stacked Evidence Trees
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009)
21. 11/10 Lee Stearns Neuroscience of Face Recognition
James V. Haxby, Elizabeth A. Hoffman, M. Ida Gobbini, "Human neural systems for face recognition and social communication".

Martha J. Farah, Carol Rabinowitz, Graham E. Quinn, Grant T. Liu, "Early Commitment of Neural Substrates for Face Recognition".
22. 11/15 Carlos Castillo Face Recognition with Pose Ahmed Ashraf, Simon Lucey, and Tsuhan Chen, Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition, CVPR 2008.

Carlos Castillo and David Jacobs.  Using Stereo Matching with General Epipolar Geometry for 2D Face Recognition across Pose.  PAMI 2009.
23. 11/17 Arpit Jain DNA-based biometrics A cryptographic biometric authentication system based on genetic fingerprints
link: -

Interpreting DNA evidence - 
24. 11/22 Ashish Shrivastava Gait Support vector regression for multi-view gait recognition based on local motion feature selection
by W. Kusakunniran, Q. Wu, J. Zhang, and H. Li.

Self-calibrating view-invariant gait biometrics by M. Goffredo, I. Bouchrika, J. Carter, and M. Nixon
25. 11/24 Cancelled Thanksgiving  
Ayman Abaza, Arun Ross, Hany Ammar

Challenges of Developing an Automated Dental Identification System
Mohamed Abdel-Mottaleb, Omaima Nomir,Diaa Eldin Nassar , Gamal Fahmy, and Hany H. Ammatr
27. 12/1 David Jacobs Speaker Identification Kinnunen, T. and Li, H. 2010.
An overview of text-independent speaker recognition: From features to supervectors.

Burget Lukáš, Matějka Pavel, Schwarz Petr, Glembek Ondřej, Černocký Jan,
"Analysis of feature extraction and channel compensation in GMM speaker recognition system",
In: IEEE Transactions on Audio, Speech, and Language Processing, 2007.
28. 12/6 David Jacobs Face Recognition Zhimin Cao, Qi Yin, Xiaoou Tang, and Jian Sun.
Face Recognition with Learning-based Descriptor.
Computer Vision and Pattern Recognition (CVPR), 2010.

Matthieu Guillaumin, Jakob Verbeek, and Cordelia Schmid.
Is that you? Metric Learning Approaches for Face Identification.
International Conference on Computer Vision (ICCV), 2009.
29. 12/8 Jacobs Conclusions  




Student Honor Code

The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council. This Code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit To further exhibit your commitment to academic integrity, remember to sign the Honor Pledge on all examinations and assignments: "I pledge on my honor that I have not given or received any unauthorized assistance on this examination (assignment)."